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Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19. | Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications. | Scientific reports | 2023-06-30T00:00:00 | [
"RezaKalantar",
"SumeetHindocha",
"BenjaminHunter",
"BhupinderSharma",
"NasirKhan",
"Dow-MuKoh",
"MerinaAhmed",
"Eric OAboagye",
"Richard WLee",
"Matthew DBlackledge"
] | 10.1038/s41598-023-36712-1 |
Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture. | COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays. The recent deep convolutional neural network (CNN) RegNetX032 was adapted for detecting COVID-19 from chest X-ray (CXR) images using polymerase chain reaction (RT-PCR) as a reference. The model was customized and trained on five datasets containing more than 15,000 CXR images (including 4148COVID-19-positive cases) and then tested on 321 images (150 COVID-19-positive) from Montfort Hospital. Twenty percent of the data from the five datasets were used as validation data for hyperparameter optimization. Each CXR image was processed by the model to detect COVID-19. Multi-binary classifications were proposed, such as: COVID-19 vs. normal, COVID-19 + pneumonia vs. normal, and pneumonia vs. normal. The performance results were based on the area under the curve (AUC), sensitivity, and specificity. In addition, an explainability model was developed that demonstrated the high performance and high generalization degree of the proposed model in detecting and highlighting the signs of the disease. The fine-tuned RegNetX032 model achieved an overall accuracy score of 96.0%, with an AUC score of 99.1%. The model showed a superior sensitivity of 98.0% in detecting signs from CXR images of COVID-19 patients, and a specificity of 93.0% in detecting healthy CXR images. A second scenario compared COVID-19 + pneumonia vs. normal (healthy X-ray) patients. The model achieved an overall score of 99.1% (AUC) with a sensitivity of 96.0% and specificity of 93.0% on the Montfort dataset. For the validation set, the model achieved an average accuracy of 98.6%, an AUC score of 98.0%, a sensitivity of 98.0%, and a specificity of 96.0% for detection (COVID-19 patients vs. healthy patients). The second scenario compared COVID-19 + pneumonia vs. normal patients. The model achieved an overall score of 98.8% (AUC) with a sensitivity of 97.0% and a specificity of 96.0%. This robust deep learning model demonstrated excellent performance in detecting COVID-19 from chest X-rays. This model could be used to automate the detection of COVID-19 and improve decision making for patient triage and isolation in hospital settings. This could also be used as a complementary aid for radiologists or clinicians when differentiating to make smart decisions. | Viruses | 2023-06-28T00:00:00 | [
"MohamedChetoui",
"Moulay AAkhloufi",
"El MostafaBouattane",
"JosephAbdulnour",
"StephaneRoux",
"Chantal D'AoustBernard"
] | 10.3390/v15061327
10.1148/radiol.2020200432
10.7326/M20-1495
10.1186/s12938-018-0544-y
10.1148/81.2.185
10.12988/ams.2015.54348
10.1371/journal.pone.0247954
10.3390/bioengineering8060084
10.1080/14737167.2020.1823221
10.2147/RMHP.S341500
10.3390/jcm11113013
10.3389/frai.2022.919672
10.1371/journal.pone.0259179
10.3390/electronics11223836
10.1016/j.imu.2022.100945
10.1080/23311916.2022.2079221
10.1038/s41598-020-71294-2
10.59275/j.melba.2020-48g7
10.1109/ACCESS.2020.3010287
10.1002/ima.22770
10.1016/j.bspc.2022.103530
10.3389/fgene.2022.980338
10.18280/mmep.090615
10.1016/j.compbiomed.2022.106065
10.1007/s10522-021-09946-7
10.1016/j.patcog.2021.108243
10.17605/OSF.IO/NH7G8 |
COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model. | The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an increasing number of patients and a lack of skilled radiologists, automated assessment of COVID-19 severity using medical image analysis has become increasingly important. Chest x-ray (CXR) imaging plays a significant role in assessing the severity of pneumonia, especially in low-resource hospitals, and is the most frequently used diagnostic imaging in the world. Previous methods that automatically predict the severity of COVID-19 pneumonia mainly focus on feature pooling from pre-trained CXR models without explicitly considering the underlying human anatomical attributes. This paper proposes an anatomy-aware (AA) deep learning model that learns the generic features from x-ray images considering the underlying anatomical information. Utilizing a pre-trained model and lung segmentation masks, the model generates a feature vector including disease-level features and lung involvement scores. We have used four different open-source datasets, along with an in-house annotated test set for training and evaluation of the proposed method. The proposed method improves the geographical extent score by 11% in terms of mean squared error (MSE) while preserving the benchmark result in lung opacity score. The results demonstrate the effectiveness of the proposed AA model in COVID-19 severity prediction from chest X-ray images. The algorithm can be used in low-resource setting hospitals for COVID-19 severity prediction, especially where there is a lack of skilled radiologists. | Journal of digital imaging | 2023-06-28T00:00:00 | [
"Nusrat BintaNizam",
"Sadi MohammadSiddiquee",
"MahbubaShirin",
"Mohammed Imamul HassanBhuiyan",
"TaufiqHasan"
] | 10.1007/s10278-023-00861-6
10.3390/s23010426
10.1007/s00330-020-07033-y
10.1001/jamanetworkopen.2020.22779
10.1016/j.chaos.2020.110495
10.1007/s10489-020-01829-7
10.3390/sym13010113
10.1148/rg.2018170048
10.1016/j.eswa.2020.114054
10.1007/s00500-020-05424-3
10.1016/j.compbiomed.2022.106331
10.1007/s10489-020-01867-1
10.1038/s41598-022-27266-9
10.1016/j.eswa.2020.113909
10.26599/BDMA.2020.9020012
10.3390/s23020743
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10.1016/j.compbiomed.2020.103792
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10.1016/j.asoc.2021.107645
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10.1016/j.compmedimag.2019.05.005
10.1109/JBHI.2022.3199594
10.1007/s10278-021-00434-5
10.1371/journal.pone.0236621
10.1016/S2589-7500(21)00039-X
10.1016/j.media.2021.102046
10.2214/ajr.174.1.1740071
10.1016/j.media.2005.02.002 |
Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction. | Since 2019, COVID-19 disease caused significant damage and it has become a serious health issue in the worldwide. The number of infected and confirmed cases is increasing day by day. Different hospitals and countries around the world to this day are not equipped enough to treat these cases and stop this pandemic evolution. Lung and chest X-ray images (e.g., radiography images) and chest CT images are the most effective imaging techniques to analyze and diagnose the COVID-19 related problems. Deep learning-based techniques have recently shown good performance in computer vision and healthcare fields. We propose developing a new deep learning-based application for COVID-19 segmentation and analysis in this work. The proposed system is developed based on the context aggregation neural network. This network consists of three main modules: the context fuse model (CFM), attention mix module (AMM) and a residual convolutional module (RCM). The developed system can detect two main COVID-19-related regions: ground glass opacity and consolidation area in CT images. Generally, these lesions are often related to common pneumonia and COVID 19 cases. Training and testing experiments have been conducted using the COVID-x-CT dataset. Based on the obtained results, the developed system demonstrated better and more competitive results compared to state-of-the-art performances. The numerical findings demonstrate the effectiveness of the proposed work by outperforming other works in terms of accuracy by a factor of over 96.23%. | Multimedia tools and applications | 2023-06-26T00:00:00 | [
"MounaAfif",
"RiadhAyachi",
"YahiaSaid",
"MohamedAtri"
] | 10.1007/s11042-023-14941-w
10.1007/s11042-022-12577-w
10.1142/S0218001421500245
10.1109/TPAMI.2016.2644615
10.3390/rs11101158
10.1109/TMM.2014.2373812
10.1148/radiol.2017162326
10.1148/radiol.2020200905
10.1186/s12880-020-00529-5
10.1007/s11042-022-12326-z
10.1016/j.patcog.2021.108498
10.1109/TGRS.2022.3170493
10.2214/AJR.20.22976 |
MediNet: transfer learning approach with MediNet medical visual database. | The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. | Multimedia tools and applications | 2023-06-26T00:00:00 | [
"Hatice CatalReis",
"VeyselTurk",
"KouroshKhoshelham",
"SerhatKaya"
] | 10.1007/s11042-023-14831-1
10.1109/ACCESS.2020.2989273
10.1016/j.dib.2019.104863
10.1038/s41598-021-83503-7
10.3390/cancers14051280
10.1186/s43057-021-00053-4
10.1016/j.compbiomed.2019.103345
10.1109/ACCESS.2019.2891970
10.1038/nature21056
10.1162/neco.1997.9.8.1735
10.3390/diagnostics12020274
10.1016/j.measurement.2020.108046
10.1109/ACCESS.2017.2788044
10.3934/mbe.2020328
10.1038/s41598-020-78129-0
10.1155/2022/7672196
10.1016/j.ins.2019.06.011
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10.1117/1.JMI.7.3.034501
10.1109/ACCESS.2020.2978629
10.1038/s41598-020-61055-6
10.1007/s00034-019-01246-3
10.1038/s41598-019-52737-x
10.1109/TIP.2021.3058783 |
COVID-19 prediction based on hybrid Inception V3 with VGG16 using chest X-ray images. | The Corona Virus was first started in the Wuhan city, China in December of 2019. It belongs to the Coronaviridae family, which can infect both animals and humans. The diagnosis of coronavirus disease-2019 (COVID-19) is typically detected by Serology, Genetic Real-Time reverse transcription-Polymerase Chain Reaction (RT-PCR), and Antigen testing. These testing methods have limitations like limited sensitivity, high cost, and long turn-around time. It is necessary to develop an automatic detection system for COVID-19 prediction. Chest X-ray is a lower-cost process in comparison to chest Computed tomography (CT). Deep learning is the best fruitful technique of machine learning, which provides useful investigation for learning and screening a large amount of chest X-ray images with COVID-19 and normal. There are many deep learning methods for prediction, but these methods have a few limitations like overfitting, misclassification, and false predictions for poor-quality chest X-rays. In order to overcome these limitations, the novel hybrid model called "Inception V3 with VGG16 (Visual Geometry Group)" is proposed for the prediction of COVID-19 using chest X-rays. It is a combination of two deep learning models, Inception V3 and VGG16 (IV3-VGG). To build the hybrid model, collected 243 images from the COVID-19 Radiography Database. Out of 243 X-rays, 121 are COVID-19 positive and 122 are normal images. The hybrid model is divided into two modules namely pre-processing and the IV3-VGG. In the dataset, some of the images with different sizes and different color intensities are identified and pre-processed. The second module i.e., IV3-VGG consists of four blocks. The first block is considered for VGG-16 and blocks 2 and 3 are considered for Inception V3 networks and final block 4 consists of four layers namely Avg pooling, dropout, fully connected, and Softmax layers. The experimental results show that the IV3-VGG model achieves the highest accuracy of 98% compared to the existing five prominent deep learning models such as Inception V3, VGG16, ResNet50, DenseNet121, and MobileNet. | Multimedia tools and applications | 2023-06-26T00:00:00 | [
"KSrinivas",
"RGagana Sri",
"KPravallika",
"KNishitha",
"Subba RaoPolamuri"
] | 10.1007/s11042-023-15903-y
10.1016/j.asoc.2019.04.031
10.1016/j.cell.2020.06.035
10.1007/s10489-020-01941-8
10.1186/s12985-015-0422-1
10.1016/j.chaos.2020.110495
10.1016/j.eswa.2020.114054
10.3390/sym12040651
10.1016/j.media.2020.101794
10.1016/j.compbiomed.2020.103792
10.1016/j.jinf.2020.03.051
10.1016/j.ijid.2020.05.098
10.1007/s10489-020-01900-3
10.1128/CVI.00355-10
10.1016/j.jinf.2020.04.022
10.1016/j.compbiomed.2020.103805
10.3201/eid2007.140296
10.3201/eid1608.100208
10.1007/s10489-020-02019-1 |
Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging. | Corona Virus (COVID-19) could be considered as one of the most devastating pandemics of the twenty-first century. The effective and the rapid screening of infected patients could reduce the mortality and even the contagion rate. Chest X-ray radiology could be designed as one of the effective screening techniques for COVID-19 exploration. In this paper, we propose an advanced approach based on deep learning architecture to automatic and effective screening techniques dedicated to the COVID-19 exploration through chest X-ray (CXR) imaging. Despite the success of state-of-the-art deep learning-based models for COVID-19 detection, they might suffer from several problems such as the huge memory and the computational requirement, the overfitting effect, and the high variance. To alleviate these issues, we investigate the Transfer Learning to the Efficient-Nets models. Next, we fine-tuned the whole network to select the optimal hyperparameters. Furthermore, in the preprocessing step, we consider an intensity-normalization method succeeded by some data augmentation techniques to solve the imbalanced dataset classes' issues. The proposed approach has presented a good performance in detecting patients attained by COVID-19 achieving an accuracy rate of 99.0% and 98% respectively using training and testing datasets. A comparative study over a publicly available dataset with the recently published deep-learning-based architectures could attest the proposed approach's performance. | Multimedia tools and applications | 2023-06-26T00:00:00 | [
"HibaMzoughi",
"InesNjeh",
"Mohamed BenSlima",
"AhmedBenHamida"
] | 10.1007/s11042-023-15097-3
10.1007/s42979-021-00981-2
10.1007/s10278-021-00431-8
10.1016/j.eswa.2020.114054
10.1007/s42600-021-00151-6
10.1007/s10044-021-00984-y
10.1016/j.ijsu.2020.02.034
10.1142/S0218001409007326
10.1109/TII.2021.3057683
10.1038/s41598-020-76550-z
10.1016/j.patcog.2017.10.002 |
Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review. | This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers. | Multimedia tools and applications | 2023-06-26T00:00:00 | [
"Yogesh HBhosale",
"K SridharPatnaik"
] | 10.1007/s11042-023-15029-1
10.1109/ACCESS.2021.3058066
10.1016/j.compbiomed.2017.09.017
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10.1016/j.compbiomed.2020.103795
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10.1007/s00259-020-04953-1 |
Deep Learning Based COVID-19 Detection via Hard Voting Ensemble Method. | Healthcare systems throughout the world are under a great deal of strain because to the continuing COVID-19 epidemic, making early and precise diagnosis critical for limiting the virus's propagation and efficiently treating victims. The utilization of medical imaging methods like X-rays can help to speed up the diagnosis procedure. Which can offer valuable insights into the virus's existence in the lungs. We present a unique ensemble approach to identify COVID-19 using X-ray pictures (X-ray-PIC) in this paper. The suggested approach, based on hard voting, combines the confidence scores of three classic deep learning models: CNN, VGG16, and DenseNet. We also apply transfer learning to enhance performance on small medical image datasets. Experiments indicate that the suggested strategy outperforms current techniques with a 97% accuracy, a 96% precision, a 100% recall, and a 98% F1-score.These results demonstrate the effectiveness of using ensemble approaches and COVID-19 transfer-learning diagnosis using X-ray-PIC, which could greatly aid in early detection and reducing the burden on global health systems. | Wireless personal communications | 2023-06-26T00:00:00 | [
"Asaad QasimShareef",
"SeferKurnaz"
] | 10.1007/s11277-023-10485-2
10.1007/s13204-021-02100-2
10.1148/radiol.2020200642
10.1016/j.ijleo.2022.170396
10.3390/sym15010123
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10.1007/s10044-021-00984-y
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10.1016/j.compbiomed.2020.103792
10.1016/j.imu.2020.100506
10.1016/j.chaos.2020.109944
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10.1016/j.compbiomed.2022.105233
10.1038/s41586-020-2008-3
10.1016/j.ejrad.2020.109041
10.1093/cid/ciaa725 |
ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs. | Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized solution to this problem. In FL, siloed data is used for the model training to ensure data privacy. In this paper, we investigate the viability of the federated approach using the detection of COVID-19 pneumonia as a use case. 1411 individual chest radiographs, sourced from the public data repository COVIDx8 are used. The dataset contains radiographs of 753 normal lung findings and 658 COVID-19 related pneumonias. We partition the data unevenly across five separate data silos in order to reflect a typical FL scenario. For the binary image classification analysis of these radiographs, we propose | Journal of healthcare informatics research | 2023-06-26T00:00:00 | [
"PascalRiedel",
"Reinholdvon Schwerin",
"DanielSchaudt",
"AlexanderHafner",
"ChristianSpäte"
] | 10.1007/s41666-023-00132-7
10.1038/s42256-022-00601-5
10.48550/ARXIV.2007.09339
10.1001/jama.2018.5630
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10.1111/tmi.13383
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10.1148/radiol.2021204522
10.1609/aaai.v33i01.33019808
10.1109/TIFS.2020.2988575 |
Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis. | When the COVID-19 pandemic commenced in 2020, scientists assisted medical specialists with diagnostic algorithm development. One scientific research area related to COVID-19 diagnosis was medical imaging and its potential to support molecular tests. Unfortunately, several systems reported high accuracy in development but did not fare well in clinical application. The reason was poor generalization, a long-standing issue in AI development. Researchers found many causes of this issue and decided to refer to them as confounders, meaning a set of artefacts and methodological errors associated with the method. We aim to contribute to this steed by highlighting an undiscussed confounder related to image resolution.
20 216 chest X-ray images (CXR) from worldwide centres were analyzed. The CXRs were bijectively projected into the 2D domain by performing Uniform Manifold Approximation and Projection (UMAP) embedding on the radiomic features (rUMAP) or CNN-based neural features (nUMAP) from the pre-last layer of the pre-trained classification neural network. Additional 44 339 thorax CXRs were used for validation. The comprehensive analysis of the multimodality of the density distribution in rUMAP/nUMAP domains and its relation to the original image properties was used to identify the main confounders.
nUMAP revealed a hidden bias of neural networks towards the image resolution, which the regular up-sampling procedure cannot compensate for. The issue appears regardless of the network architecture and is not observed in a high-resolution dataset. The impact of the resolution heterogeneity can be partially diminished by applying advanced deep-learning-based super-resolution networks.
rUMAP and nUMAP are great tools for image homogeneity analysis and bias discovery, as demonstrated by applying them to COVID-19 image data. Nonetheless, nUMAP could be applied to any type of data for which a deep neural network could be constructed. Advanced image super-resolution solutions are needed to reduce the impact of the resolution diversity on the classification network decision. | Computer methods and programs in biomedicine | 2023-06-26T00:00:00 | [
"MarekSocha",
"WojciechPrażuch",
"AleksandraSuwalska",
"PawełFoszner",
"JoannaTobiasz",
"JerzyJaroszewicz",
"KatarzynaGruszczynska",
"MagdalenaSliwinska",
"MateuszNowak",
"BarbaraGizycka",
"GabrielaZapolska",
"TadeuszPopiela",
"GrzegorzPrzybylski",
"PiotrFiedor",
"MalgorzataPawlowska",
"RobertFlisiak",
"KrzysztofSimon",
"JerzyWalecki",
"AndrzejCieszanowski",
"EdytaSzurowska",
"MichalMarczyk",
"JoannaPolanska",
"NoneNone"
] | 10.1016/j.cmpb.2023.107684
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10.2217/fvl-2020-0130
10.1016/j.cmpb.2020.105581
10.1007/s10489-020-01829-7
10.1016/j.compbiomed.2020.103792
10.1038/s41598-020-76550-z
10.1038/s42256-021-00307-0
10.1109/IMCET53404.2021.9665574
10.1007/s12553-021-00520-2
10.1148/ryai.220028
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10.1109/PRAI53619.2021.9551043
10.1109/ACCESS.2019.2918926
10.1148/ryai.2019190015 |
LayNet-A multi-layer architecture to handle imbalance in medical imaging data. | In an imbalanced dataset, a machine learning classifier using traditional imbalance handling methods may achieve good accuracy, but in highly imbalanced datasets, it may over-predict the majority class and ignore the minority class. In the medical domain, failing to correctly estimate the minority class might lead to a false negative, which is concerning in cases of life-threatening illnesses and infectious diseases like Covid-19. Currently, classification in deep learning has a single layered architecture where a neural network is employed. This paper proposes a multilayer design entitled LayNet to address this issue. LayNet aims to lessen the class imbalance by dividing the classes among layers and achieving a balanced class distribution at each layer. To ensure that all the classes are being classified, minor classes are combined to form a single new 'hybrid' class at higher layers. The final layer has no hybrid class and only singleton(distinct) classes. Each layer of the architecture includes a separate model that determines if an input belongs to one class or a hybrid class. If it fits into the hybrid class, it advances to the following layer, which is further categorized within the hybrid class. The method to divide the classes into various architectural levels is also introduced in this paper. The Ocular Disease Intelligent Recognition Dataset, Covid-19 Radiography Dataset, and Retinal OCT Dataset are used to evaluate this methodology. The LayNet architecture performs better on these datasets when the results of the traditional single-layer architecture and the proposed multilayered architecture are compared. | Computers in biology and medicine | 2023-06-25T00:00:00 | [
"JayJani",
"JayDoshi",
"IshitaKheria",
"KarishniMehta",
"ChetashriBhadane",
"RuhinaKarani"
] | 10.1016/j.compbiomed.2023.107179 |
Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. | The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable. | Applied soft computing | 2023-06-22T00:00:00 | [
"NavidGhassemi",
"AfshinShoeibi",
"MarjaneKhodatars",
"JonathanHeras",
"AlirezaRahimi",
"AssefZare",
"Yu-DongZhang",
"Ram BilasPachori",
"J ManuelGorriz"
] | 10.1016/j.asoc.2023.110511
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10.1016/j.patrec.2021.11.020 |
To segment or not to segment: COVID-19 detection for chest X-rays. | Artificial intelligence (AI) has been integrated into most technologies we use. One of the most promising applications in AI is medical imaging. Research demonstrates that AI has improved the performance of most medical imaging analysis systems. Consequently, AI has become a fundamental element of the state of the art with improved outcomes across a variety of medical imaging applications. Moreover, it is believed that computer vision (CV) algorithms are highly effective for image analysis. Recent advances in CV facilitate the recognition of patterns in medical images. In this manner, we investigate CV segmentation techniques for COVID-19 analysis. We use different segmentation techniques, such as k-means, U-net, and flood fill, to extract the lung region from CXRs. Afterwards, we compare the effectiveness of these three segmentation approaches when applied to CXRs. Then, we use machine learning (ML) and deep learning (DL) models to identify COVID-19 lesion molecules in both healthy and pathological lung x-rays. We evaluate our ML and DL findings in the context of CV techniques. Our results indicate that the segmentation-related CV techniques do not exhibit comparable performance to DL and ML techniques. The most optimal AI algorithm yields an accuracy range of 0.92-0.94, whereas the addition of CV algorithms leads to a reduction in accuracy to approximately the range of 0.81-0.88. In addition, we test the performance of DL models under real-world noise, such as salt and pepper noise, which negatively impacts the overall performance. | Informatics in medicine unlocked | 2023-06-22T00:00:00 | [
"SaraAl Hajj Ibrahim",
"KhalilEl-Khatib"
] | 10.1016/j.imu.2023.101280 |
CT medical image segmentation algorithm based on deep learning technology. | For the problems of blurred edges, uneven background distribution, and many noise interferences in medical image segmentation, we proposed a medical image segmentation algorithm based on deep neural network technology, which adopts a similar U-Net backbone structure and includes two parts: encoding and decoding. Firstly, the images are passed through the encoder path with residual and convolutional structures for image feature information extraction. We added the attention mechanism module to the network jump connection to address the problems of redundant network channel dimensions and low spatial perception of complex lesions. Finally, the medical image segmentation results are obtained using the decoder path with residual and convolutional structures. To verify the validity of the model in this paper, we conducted the corresponding comparative experimental analysis, and the experimental results show that the DICE and IOU of the proposed model are 0.7826, 0.9683, 0.8904, 0.8069, and 0.9462, 0.9537 for DRIVE, ISIC2018 and COVID-19 CT datasets, respectively. The segmentation accuracy is effectively improved for medical images with complex shapes and adhesions between lesions and normal tissues. | Mathematical biosciences and engineering : MBE | 2023-06-16T00:00:00 | [
"TongpingShen",
"FangliangHuang",
"XusongZhang"
] | 10.3934/mbe.2023485 |
COV-MobNets: a mobile networks ensemble model for diagnosis of COVID-19 based on chest X-ray images. | The medical profession is facing an excessive workload, which has led to the development of various Computer-Aided Diagnosis (CAD) systems as well as Mobile-Aid Diagnosis (MAD) systems. These technologies enhance the speed and accuracy of diagnoses, particularly in areas with limited resources or remote regions during the pandemic. The primary purpose of this research is to predict and diagnose COVID-19 infection from chest X-ray images by developing a mobile-friendly deep learning framework, which has the potential for deployment in portable devices such as mobile or tablet, especially in situations where the workload of radiology specialists may be high. Moreover, this could improve the accuracy and transparency of population screening to assist radiologists during the pandemic.
In this study, the Mobile Networks ensemble model called COV-MobNets is proposed to classify positive COVID-19 X-ray images from negative ones and can have an assistant role in diagnosing COVID-19. The proposed model is an ensemble model, combining two lightweight and mobile-friendly models: MobileViT based on transformer structure and MobileNetV3 based on Convolutional Neural Network. Hence, COV-MobNets can extract the features of chest X-ray images in two different methods to achieve better and more accurate results. In addition, data augmentation techniques were applied to the dataset to avoid overfitting during the training process. The COVIDx-CXR-3 benchmark dataset was used for training and evaluation.
The classification accuracy of the improved MobileViT and MobileNetV3 models on the test set has reached 92.5% and 97%, respectively, while the accuracy of the proposed model (COV-MobNets) has reached 97.75%. The sensitivity and specificity of the proposed model have also reached 98.5% and 97%, respectively. Experimental comparison proves the result is more accurate and balanced than other methods.
The proposed method can distinguish between positive and negative COVID-19 cases more accurately and quickly. The proposed method proves that utilizing two automatic feature extractors with different structures as an overall framework of COVID-19 diagnosis can lead to improved performance, enhanced accuracy, and better generalization to new or unseen data. As a result, the proposed framework in this study can be used as an effective method for computer-aided diagnosis and mobile-aided diagnosis of COVID-19. The code is available publicly for open access at https://github.com/MAmirEshraghi/COV-MobNets . | BMC medical imaging | 2023-06-16T00:00:00 | [
"Mohammad AmirEshraghi",
"AhmadAyatollahi",
"Shahriar BaradaranShokouhi"
] | 10.1186/s12880-023-01039-w
10.3390/app10165683
10.7717/PEERJ-CS.364
10.1016/j.patrec.2020.09.010
10.1038/s41598-020-76550-z
10.1109/ACCESS.2020.2994762
10.48550/arxiv.2010.11929
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10.48550/arxiv.2206.03671
10.3390/e23111383
10.48550/arxiv.1704.04861
10.48550/arxiv.2110.02178
10.3390/electronics12010223 |
A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images. | Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their application in many image processing applications. The structure of CNNs greatly affects the performance of these algorithms both in terms of accuracy and computational cost, thus, finding the best architecture for these networks is a crucial task before they are employed. In this paper, we develop a genetic programming approach for the optimization of CNN structure in diagnosing COVID-19 cases via X-ray images. A graph representation for CNN architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. The proposed architecture of CNNs is defined by two sets of parameters, one is the skeleton which determines the arrangement of the convolutional and pooling operators and their connections and one is the numerical parameters of the operators which determine the properties of these operators like filter size and kernel size. The proposed algorithm in this paper optimizes the skeleton and the numerical parameters of the CNN architectures in a co-evolutionary scheme. The proposed algorithm is used to identify covid-19 cases via X-ray images. | Artificial intelligence in medicine | 2023-06-15T00:00:00 | [
"Mohammad Hassan TayaraniNajaran"
] | 10.1016/j.artmed.2023.102571
10.1109/TIP.2015.2475625
10.1109/TEVC.2011.2163638 |
Artificial Intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems. | We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest computed tomography (CT) scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19in comparison to semi-quantitative visual scoring systems.
A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcomewas clinical deterioration, the composite endpoint including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.
The final population comprised 743 patients (mean age 65 ± 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739,
Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time.
Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice. | The British journal of radiology | 2023-06-13T00:00:00 | [
"KajetanGrodecki",
"AdityaKillekar",
"JuditSimon",
"AndrewLin",
"SebastienCadet",
"PriscillaMcElhinney",
"CatoChan",
"Michelle CWilliams",
"Barry DPressman",
"PeterJulien",
"DebiaoLi",
"PeterChen",
"NicolaGaibazzi",
"UditThakur",
"ElisabettaMancini",
"CeciliaAgalbato",
"JiroMunechika",
"HidenariMatsumoto",
"RobertoMene",
"GianfrancoParati",
"FrancoCernigliaro",
"NiteshNerlekar",
"CamillaTorlasco",
"GianlucaPontone",
"PalMaurovich-Horvat",
"Piotr JSlomka",
"DaminiDey"
] | 10.1259/bjr.20220180 |
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. | During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process. | Nature biomedical engineering | 2023-06-13T00:00:00 | [
"Hong-YuZhou",
"YizhouYu",
"ChengdiWang",
"ShuZhang",
"YuanxuGao",
"JiaPan",
"JunShao",
"GuangmingLu",
"KangZhang",
"WeiminLi"
] | 10.1038/s41551-023-01045-x
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10.1002/mef2.43
10.1038/s41586-023-05881-4
10.1007/s00330-020-07044-9 |
Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images. | Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%. | Sensors (Basel, Switzerland) | 2023-06-10T00:00:00 | [
"AmgothuShanthi",
"SrinivasKoppu"
] | 10.3390/s23115316
10.1148/radiol.2020200343
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10.1109/TMI.2020.2996645
10.3390/electronics9101634 |
A Review Paper about Deep Learning for Medical Image Analysis. | Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. | Computational and mathematical methods in medicine | 2023-06-07T00:00:00 | [
"BagherSistaninejhad",
"HabibRasi",
"ParisaNayeri"
] | 10.1155/2023/7091301
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10.1016/j.cmpb.2020.105876
10.1186/s12880-021-00728-8
10.1109/TPAMI.2018.2844175
10.1016/j.cmpb.2021.106141
10.1007/s13244-018-0639-9
10.1109/5.726791
10.1145/3065386
10.1109/ACCESS.2021.3131741
10.1007/s10278-020-00371-9
10.3390/s21175704
10.3390/s20164373
10.1016/j.eswa.2020.113274
10.24018/ejece.2021.5.1.268
10.3390/sym12111787
10.1145/3422622
10.1016/j.clinimag.2020.10.014
10.1155/2021/9956983
10.1155/2021/5536903
10.1016/j.bspc.2021.102901
10.1016/j.mehy.2020.109684
10.3390/app10020559
10.3390/electronics9071066
10.1109/ACCESS.2021.3079204
10.1007/978-3-030-73689-7_52
10.1155/2021/6296811
10.1016/j.aej.2021.03.048
10.1007/s13534-020-00168-3
10.3390/diagnostics11112147
10.3390/s20205736
10.1016/j.cmpb.2021.106018
10.3390/biomedicines10020223
10.1007/s13369-020-04480-z
10.1016/j.compbiomed.2020.103884
10.1109/TPAMI.2017.2699184
10.1038/s41598-020-76550-z
10.1148/radiol.2018180736
10.1007/s42600-021-00151-6
10.1109/ACCESS.2020.2994762
10.1109/TMI.2020.2993291
10.3390/s21041480
10.1155/2021/5528441
10.1109/TCBB.2020.3009859
10.1109/ACCESS.2020.3016780
10.1155/2020/8460493
10.3390/app10207233
10.1007/s11548-020-02266-0
10.1007/s11042-021-10935-8 |
Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images. | COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset. | Journal of King Saud University. Computer and information sciences | 2023-06-05T00:00:00 | [
"Chiagoziem CUkwuoma",
"DongshengCai",
"Md Belal BinHeyat",
"OlusolaBamisile",
"HumphreyAdun",
"ZaidAl-Huda",
"Mugahed AAl-Antari"
] | 10.1016/j.jksuci.2023.101596 |
POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021). | The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions. | Scientific data | 2023-06-03T00:00:00 | [
"AleksandraSuwalska",
"JoannaTobiasz",
"WojciechPrazuch",
"MarekSocha",
"PawelFoszner",
"DamianPiotrowski",
"KatarzynaGruszczynska",
"MagdalenaSliwinska",
"JerzyWalecki",
"TadeuszPopiela",
"GrzegorzPrzybylski",
"MateuszNowak",
"PiotrFiedor",
"MalgorzataPawlowska",
"RobertFlisiak",
"KrzysztofSimon",
"GabrielaZapolska",
"BarbaraGizycka",
"EdytaSzurowska",
"NoneNone",
"MichalMarczyk",
"AndrzejCieszanowski",
"JoannaPolanska"
] | 10.1038/s41597-023-02229-5
10.1038/s41591-021-01381-y
10.1038/s41579-020-00461-z
10.1136/bmj.m2426
10.1148/radiol.2020201160
10.1038/s41598-020-76550-z
10.1016/j.media.2020.101794
10.1016/j.eswa.2020.114054
10.1016/j.media.2021.102225
10.1016/j.cell.2018.02.010
10.1109/ACCESS.2020.3010287
10.1016/j.media.2021.102216
10.1109/TNB.2017.2676725
10.7303/syn50877085
10.1002/cem.1123 |
Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. | During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis. | Cureus | 2023-06-02T00:00:00 | [
"RufaidahDabbagh",
"AmrJamal",
"Jakir HossainBhuiyan Masud",
"Maher ATiti",
"Yasser SAmer",
"AfnanKhayat",
"Taha SAlhazmi",
"LayalHneiny",
"Fatmah ABaothman",
"MetabAlkubeyyer",
"Samina AKhan",
"Mohamad-HaniTemsah"
] | 10.7759/cureus.38373 |
A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs. | The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods. | Heliyon | 2023-05-30T00:00:00 | [
"Saleh AteeqAlmutairi"
] | 10.1016/j.heliyon.2023.e16552 |
Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography. | Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography. | Healthcare (Basel, Switzerland) | 2023-05-27T00:00:00 | [
"Kuo-HsuanLin",
"Nan-HanLu",
"TakahideOkamoto",
"Yung-HuiHuang",
"Kuo-YingLiu",
"AkariMatsushima",
"Che-ChengChang",
"Tai-BeenChen"
] | 10.3390/healthcare11101367
10.1177/1063293X211021435
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10.1016/j.asoc.2020.106580
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10.48550/arXiv.1804.02767
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10.1145/3551690.3551695
10.1007/s40747-020-00216-6
10.7717/peerj-cs.306
10.1016/j.cell.2018.02.010 |
A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling. | COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient's life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model. | Diagnostics (Basel, Switzerland) | 2023-05-27T00:00:00 | [
"Gouri ShankarChakraborty",
"SalilBatra",
"AmanSingh",
"GhulamMuhammad",
"Vanessa YelamosTorres",
"MakulMahajan"
] | 10.3390/diagnostics13101806
10.1016/j.knosys.2022.108207
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10.1016/j.bspc.2022.103772
10.1101/2020.08.20.20178913
10.1007/s00530-021-00794-6
10.1117/1.JEI.31.4.041212
10.36548/jtcsst.2021.4.004
10.1109/ACCESS.2020.3028012
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10.31224/osf.io/wx89s
10.1007/s10489-020-01904-z
10.1016/j.asoc.2021.107947
10.1145/3453170 |
CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images. | The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body's internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size). | Diagnostics (Basel, Switzerland) | 2023-05-27T00:00:00 | [
"UzairIqbal",
"RomilImtiaz",
"Abdul Khader JilaniSaudagar",
"Khubaib AmjadAlam"
] | 10.3390/diagnostics13101783
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10.3390/sym14020194
10.32604/cmc.2021.018472
10.3390/s21165571
10.1007/s11280-022-01046-x |
A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. | Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification. | Diagnostics (Basel, Switzerland) | 2023-05-27T00:00:00 | [
"SoheilaAbbasi Habashi",
"MuratKoyuncu",
"RoohallahAlizadehsani"
] | 10.3390/diagnostics13101749
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COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection. | The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. | Diagnostics (Basel, Switzerland) | 2023-05-27T00:00:00 | [
"Ibtihal A LAlablani",
"Mohammed J FAlenazi"
] | 10.3390/diagnostics13101675
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Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images. | COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases. But, visual inspection of CT images takes time and effort. In this study, we employ Convolution Neural Network (CNN) to detect coronavirus infection from CT images. The proposed study utilized transfer learning on the three pre-trained deep CNN models, namely VGG-16, ResNet, and wide ResNet, to diagnose and detect COVID-19 infection from the CT images. However, when the pre-trained models are retrained, the model suffers the generalization capability to categorize the data in the original datasets. The novel aspect of this work is the integration of deep CNN architectures with Learning without Forgetting (LwF) to enhance the model's generalization capabilities on both trained and new data samples. The LwF makes the network use its learning capabilities in training on the new dataset while preserving the original competencies. The deep CNN models with the LwF model are evaluated on original images and CT scans of individuals infected with Delta-variant of the SARS-CoV-2 virus. The experimental results show that of the three fine-tuned CNN models with the LwF method, the wide ResNet model's performance is superior and effective in classifying original and delta-variant datasets with an accuracy of 93.08% and 92.32%, respectively. | Scientific reports | 2023-05-26T00:00:00 | [
"MalligaSubramanian",
"Veerappampalayam EaswaramoorthySathishkumar",
"JaehyukCho",
"KogilavaniShanmugavadivel"
] | 10.1038/s41598-023-34908-z
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A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis. | In today's digital world, information is growing along with the expansion of Internet usage worldwide. As a consequence, bulk of data is generated constantly which is known to be "Big Data". One of the most evolving technologies in twenty-first century is Big Data analytics, it is promising field for extracting knowledge from very large datasets and enhancing benefits while lowering costs. Due to the enormous success of big data analytics, the healthcare sector is increasingly shifting toward adopting these approaches to diagnose diseases. Due to the recent boom in medical big data and the development of computational methods, researchers and practitioners have gained the ability to mine and visualize medical big data on a larger scale. Thus, with the aid of integration of big data analytics in healthcare sectors, precise medical data analysis is now feasible with early sickness detection, health status monitoring, patient treatment, and community services is now achievable. With all these improvements, a deadly disease COVID is considered in this comprehensive review with the intention of offering remedies utilizing big data analytics. The use of big data applications is vital to managing pandemic conditions, such as predicting outbreaks of COVID-19 and identifying cases and patterns of spread of COVID-19. Research is still being done on leveraging big data analytics to forecast COVID-19. But precise and early identification of COVID disease is still lacking due to the volume of medical records like dissimilar medical imaging modalities. Meanwhile, Digital imaging has now become essential to COVID diagnosis, but the main challenge is the storage of massive volumes of data. Taking these limitations into account, a comprehensive analysis is presented in the systematic literature review (SLR) to provide a deeper understanding of big data in the field of COVID-19. | New generation computing | 2023-05-25T00:00:00 | [
"NagamaniTenali",
"Gatram Rama MohanBabu"
] | 10.1007/s00354-023-00211-8
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10.1007/s00354-014-0407-4 |
Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. | Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease. | New generation computing | 2023-05-25T00:00:00 | [
"RanaKhattab",
"Islam RAbdelmaksoud",
"SamirAbdelrazek"
] | 10.1007/s00354-023-00213-6
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10.1016/j.cell.2018.02.010 |
COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network. | This study proposes a deep-learning-based solution (named CapsNetCovid) for COVID-19 diagnosis using a capsule neural network (CapsNet). CapsNets are robust for image rotations and affine transformations, which is advantageous when processing medical imaging datasets. This study presents a performance analysis of CapsNets on standard images and their augmented variants for binary and multi-class classification. CapsNetCovid was trained and evaluated on two COVID-19 datasets of CT images and X-ray images. It was also evaluated on eight augmented datasets. The results show that the proposed model achieved classification accuracy, precision, sensitivity, and F1-score of 99.929%, 99.887%, 100%, and 99.319%, respectively, for the CT images. It also achieved a classification accuracy, precision, sensitivity, and F1-score of 94.721%, 93.864%, 92.947%, and 93.386%, respectively, for the X-ray images. This study presents a comparative analysis between CapsNetCovid, CNN, DenseNet121, and ResNet50 in terms of their ability to correctly identify randomly transformed and rotated CT and X-ray images without the use of data augmentation techniques. The analysis shows that CapsNetCovid outperforms CNN, DenseNet121, and ResNet50 when trained and evaluated on CT and X-ray images without data augmentation. We hope that this research will aid in improving decision making and diagnostic accuracy of medical professionals when diagnosing COVID-19. | Diagnostics (Basel, Switzerland) | 2023-05-16T00:00:00 | [
"Andronicus AAkinyelu",
"BubacarrBah"
] | 10.3390/diagnostics13081484
10.3389/frai.2022.919672
10.1007/s10586-022-03703-2
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10.1038/s41598-020-76550-z |
A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning. | Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models. | Sensors (Basel, Switzerland) | 2023-05-13T00:00:00 | [
"AbidaSultana",
"MdNahiduzzaman",
"Sagor ChandroBakchy",
"Saleh MohammedShahriar",
"Hasibul IslamPeyal",
"Muhammad E HChowdhury",
"AmithKhandakar",
"MohamedArselene Ayari",
"MominulAhsan",
"JulfikarHaider"
] | 10.3390/s23094458
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10.1016/j.cmpb.2020.105581 |
A Lightweight AMResNet Architecture with an Attention Mechanism for Diagnosing COVID-19. | COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. We proposed a lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19, named AMResNet.
COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered.
A lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19.
By introducing the channel attention mechanism and image spatial information attention mechanism, a better level can be achieved without increasing the number of model parameters.
In the collected data sets, we achieved an average accuracy rate of more than 92%, and the sensitivity and specificity of specific disease categories were also above 90%.
The convolution neural network framework can be used as a novel method for artificial intelligence to diagnose COVID-19 or other diseases based on medical images. | Current medical imaging | 2023-05-12T00:00:00 | [
"QiZhou",
"Jamal AlzobairHammad Kowah",
"HuijunLi",
"MingqingYuan",
"LiheJiang",
"XuLiu"
] | 10.2174/1573405620666230426121437 |
Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records. | An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensemble model for COVID-19 using AI models with initial chest X-ray and electronic health record (EHR) data.
We used convolutional neural network (CNN) models (Inception-ResNet-V2 and EfficientNet) for chest X-ray analysis and multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and random forest (RF) models for EHR data analysis. The Gradient-weighted Class Activation Mapping and Shapley Additive Explanations (SHAP) methods were used to determine the effects of these features on COVID-19. We developed an ensemble model (Area under the receiver operating characteristic curve of 0.8698) using a soft voting method with weight differences for CNN, XGBoost, MLP, and RF models. To resolve the data imbalance, we conducted F1-score optimization by adjusting the cutoff values to optimize the model performance (F1 score of 0.77).
Our study is meaningful in that we developed an early mortality prediction model using only the initial chest X-ray and EHR data of COVID-19 patients. Early prediction of the clinical courses of patients is helpful for not only treatment but also bed management. Our results confirmed the performance improvement of the ensemble model achieved by combining AI models. Through the SHAP method, laboratory tests that indicate the factors affecting COVID-19 mortality were discovered, highlighting the importance of these tests in managing COVID-19 patients. | BMC bioinformatics | 2023-05-10T00:00:00 | [
"Seung MinBaik",
"Kyung SookHong",
"Dong JinPark"
] | 10.1186/s12859-023-05321-0
10.1016/j.radi.2020.09.010
10.3390/diagnostics12040920
10.3390/diagnostics12040821
10.1002/jmv.27352
10.1371/journal.pone.0252384
10.1371/journal.pone.0249285
10.3389/fcvm.2021.638011
10.1109/jbhi.2020.3012383
10.1007/978-3-030-33128-3_1
10.1111/cyt.12942
10.1007/s13244-018-0639-9
10.1007/s12559-020-09751-3
10.1016/j.compbiomed.2020.103792
10.1007/s00500-020-05424-3
10.1007/s00330-021-08049-8
10.1016/s2589-7500(21)00039-x
10.3389/fpsyg.2021.651398
10.1186/s40462-021-00245-x
10.1016/j.jneumeth.2021.109098
10.1016/j.compbiomed.2022.105550
10.3389/fmed.2021.676343
10.1002/jcla.24053
10.1002/jmv.26082
10.1016/j.mayocp.2020.04.006
10.3389/fpubh.2022.857368
10.3389/fcvm.2021.697737
10.1109/tpami.2021.3083089
10.1007/s10439-018-02116-w
10.1038/s41598-021-87171-5
10.1016/j.compbiomed.2021.104829
10.3390/s21227475
10.1007/s00521-021-06177-2
10.2174/1573409914666180828105228
10.1001/jamapsychiatry.2019.3671
10.1016/j.envpol.2019.06.088 |
COVID-19 disease identification network based on weakly supervised feature selection. | The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance. | Mathematical biosciences and engineering : MBE | 2023-05-10T00:00:00 | [
"JingyaoLiu",
"QingheFeng",
"YuMiao",
"WeiHe",
"WeiliShi",
"ZhengangJiang"
] | 10.3934/mbe.2023409 |
An efficient, lightweight MobileNetV2-based fine-tuned model for COVID-19 detection using chest X-ray images. | In recent years, deep learning's identification of cancer, lung disease and heart disease, among others, has contributed to its rising popularity. Deep learning has also contributed to the examination of COVID-19, which is a subject that is currently the focus of considerable scientific debate. COVID-19 detection based on chest X-ray (CXR) images primarily depends on convolutional neural network transfer learning techniques. Moreover, the majority of these methods are evaluated by using CXR data from a single source, which makes them prohibitively expensive. On a variety of datasets, current methods for COVID-19 detection may not perform as well. Moreover, most current approaches focus on COVID-19 detection. This study introduces a rapid and lightweight MobileNetV2-based model for accurate recognition of COVID-19 based on CXR images; this is done by using machine vision algorithms that focused largely on robust and potent feature-learning capabilities. The proposed model is assessed by using a dataset obtained from various sources. In addition to COVID-19, the dataset includes bacterial and viral pneumonia. This model is capable of identifying COVID-19, as well as other lung disorders, including bacterial and viral pneumonia, among others. Experiments with each model were thoroughly analyzed. According to the findings of this investigation, MobileNetv2, with its 92% and 93% training validity and 88% precision, was the most applicable and reliable model for this diagnosis. As a result, one may infer that this study has practical value in terms of giving a reliable reference to the radiologist and theoretical significance in terms of establishing strategies for developing robust features with great presentation ability. | Mathematical biosciences and engineering : MBE | 2023-05-10T00:00:00 | [
"ShubashiniVelu"
] | 10.3934/mbe.2023368 |
Data augmentation based semi-supervised method to improve COVID-19 CT classification. | The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL. | Mathematical biosciences and engineering : MBE | 2023-05-10T00:00:00 | [
"XiangtaoChen",
"YutingBai",
"PengWang",
"JiaweiLuo"
] | 10.3934/mbe.2023294 |
A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative. | Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).
To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.
The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.
A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.
We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans. | PloS one | 2023-05-02T00:00:00 | [
"LaurensTopff",
"JoséSánchez-García",
"RafaelLópez-González",
"Ana JiménezPastor",
"Jacob JVisser",
"MerelHuisman",
"JulienGuiot",
"Regina G HBeets-Tan",
"AngelAlberich-Bayarri",
"AlmudenaFuster-Matanzo",
"Erik RRanschaert",
"NoneNone"
] | 10.1371/journal.pone.0285121
10.1186/s12941-021-00438-7
10.1038/s41576-021-00360-w
10.1016/j.talanta.2022.123409
10.1007/s15010-022-01819-6
10.1148/radiol.2020201365
10.1186/s13244-021-01096-1
10.1016/j.diii.2020.11.008
10.1148/radiol.2020200642
10.1148/radiol.2020200343
10.1259/bjr.20201039
10.1183/13993003.00398-2020
10.1183/13993003.00334-2020
10.1016/S1473-3099(20)30134-1
10.1016/S1473-3099(20)30086-4
10.1016/j.radi.2020.09.010
10.1016/j.ejrad.2020.108961
10.1016/j.ejrad.2019.108774
10.1148/radiol.2021203957
10.1016/j.ejmp.2021.06.001
10.1148/radiol.2020200370
10.3389/fmed.2021.704256
10.1148/radiol.2020201491
10.1016/j.cell.2020.04.045
10.1038/s41598-020-76282-0
10.1007/s00330-021-07715-1
10.1183/13993003.00775-2020
10.1007/s00330-020-07033-y
10.1148/ryct.2020200389
10.1007/s00330-020-07013-2
10.1148/ryct.2020200047
10.3389/fmed.2022.930055
10.1007/s11042-021-11153-y
10.1038/s41598-022-06854-9
10.1016/j.ejro.2020.100272 |
Contemporary Concise Review 2022: Interstitial lung disease. | Novel genetic associations for idiopathic pulmonary fibrosis (IPF) risk have been identified. Common genetic variants associated with IPF are also associated with chronic hypersensitivity pneumonitis. The characterization of underlying mechanisms, such as pathways involved in myofibroblast differentiation, may reveal targets for future treatments. Newly identified circulating biomarkers are associated with disease progression and mortality. Deep learning and machine learning may increase accuracy in the interpretation of CT scans. Novel treatments have shown benefit in phase 2 clinical trials. Hospitalization with COVID-19 is associated with residual lung abnormalities in a substantial number of patients. Inequalities exist in delivering and accessing interstitial lung disease specialist care. | Respirology (Carlton, Vic.) | 2023-05-01T00:00:00 | [
"David J FSmith",
"R GisliJenkins"
] | 10.1111/resp.14511 |
Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis. | In this paper, a novel deep learning-based medical imaging analysis framework is developed, which aims to deal with the insufficient feature learning caused by the imperfect property of imaging data. Named as multi-scale efficient network (MEN), the proposed method integrates different attention mechanisms to realize sufficient extraction of both detailed features and semantic information in a progressive learning manner. In particular, a fused-attention block is designed to extract fine-grained details from the input, where the squeeze-excitation (SE) attention mechanism is applied to make the model focus on potential lesion areas. A multi-scale low information loss (MSLIL)-attention block is proposed to compensate for potential global information loss and enhance the semantic correlations among features, where the efficient channel attention (ECA) mechanism is adopted. The proposed MEN is comprehensively evaluated on two COVID-19 diagnostic tasks, and the results show that as compared with some other advanced deep learning models, the proposed method is competitive in accurate COVID-19 recognition, which yields the best accuracy of 98.68% and 98.85%, respectively, and exhibits satisfactory generalization ability as well. | Computers in biology and medicine | 2023-04-27T00:00:00 | [
"TingyiXie",
"ZidongWang",
"HanLi",
"PeishuWu",
"HuixiangHuang",
"HongyiZhang",
"Fuad EAlsaadi",
"NianyinZeng"
] | 10.1016/j.compbiomed.2023.106947
10.1080/00207721.2022.2083262 |
A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19). | A COVID-19, caused by SARS-CoV-2, has been declared a global pandemic by WHO. It first appeared in China at the end of 2019 and quickly spread throughout the world. During the third layer, it became more critical. COVID-19 spread is extremely difficult to control, and a huge number of suspected cases must be screened for a cure as soon as possible. COVID-19 laboratory testing takes time and can result in significant false negatives. To combat COVID-19, reliable, accurate and fast methods are urgently needed. The commonly used Reverse Transcription Polymerase Chain Reaction has a low sensitivity of approximately 60% to 70%, and sometimes even produces negative results. Computer Tomography (CT) has been observed to be a subtle approach to detecting COVID-19, and it may be the best screening method. The scanned image's quality, which is impacted by motion-induced Poisson or Impulse noise, is vital. In order to improve the quality of the acquired image for post segmentation, a novel Impulse and Poisson noise reduction method employing boundary division max/min intensities elimination along with an adaptive window size mechanism is proposed. In the second phase, a number of CNN techniques are explored for detecting COVID-19 from CT images and an Assessment Fusion Based model is proposed to predict the result. The AFM combines the results for cutting-edge CNN architectures and generates a final prediction based on choices. The empirical results demonstrate that our proposed method performs extensively and is extremely useful in actual diagnostic situations. | Scientific reports | 2023-04-24T00:00:00 | [
"Sajid UllahKhan",
"ImdadUllah",
"NajeebUllah",
"SajidShah",
"Mohammed ElAffendi",
"BumshikLee"
] | 10.1038/s41598-023-33614-0
10.1038/s41586-020-2008-3
10.1016/S0140-6736(20)30183-5
10.22207/JPAM.14.SPL1.40
10.1007/s12098-020-03263-6
10.1148/radiol.2020200490
10.1148/radiol.2020200527
10.1148/radiol.2020200343
10.1016/S1473-3099(20)30134-1
10.1016/j.media.2017.07.005
10.1109/ACCESS.2017.2788044
10.1146/annurev-bioeng-071516-044442
10.1038/s41591-018-0268-3
10.1016/j.compbiomed.2017.08.022
10.1109/TIP.2005.871129
10.1109/5.192071
10.1145/358198.358222
10.1109/31.83870
10.1109/83.902289
10.3390/app12147092
10.32604/cmc.2022.029134
10.1038/s41598-022-25539-x
10.1038/s41598-021-99015-3
10.3390/ijerph19042013 |
Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey. | Radiomics is the high-throughput extraction of mineable and-possibly-reproducible quantitative imaging features from medical imaging. The aim of this work is to perform an unbiased bibliometric analysis on Radiomics 10 years after the first work became available, to highlight its status, pitfalls, and growing interest.
Scopus database was used to investigate all the available English manuscripts about Radiomics. R Bibliometrix package was used for data analysis: a cumulative analysis of document categories, authors affiliations, country scientific collaborations, institution collaboration networks, keyword analysis, comprehensive of co-occurrence network, thematic map analysis, and 2021 sub-analysis of trend topics was performed.
A total of 5623 articles and 16,833 authors from 908 different sources have been identified. The first available document was published in March 2012, while the most recent included was released on the 31st of December 2021. China and USA were the most productive countries. Co-occurrence network analysis identified five words clusters based on top 50 authors' keywords: Radiomics, computed tomography, radiogenomics, deep learning, tomography. Trend topics analysis for 2021 showed an increased interest in artificial intelligence (n = 286), nomogram (n = 166), hepatocellular carcinoma (n = 125), COVID-19 (n = 63), and X-ray computed (n = 60).
Our work demonstrates the importance of bibliometrics in aggregating information that otherwise would not be available in a granular analysis, detecting unknown patterns in Radiomics publications, while highlighting potential developments to ensure knowledge dissemination in the field and its future real-life applications in the clinical practice.
This work aims to shed light on the state of the art in radiomics, which offers numerous tangible and intangible benefits, and to encourage its integration in the contemporary clinical practice for more precise imaging analysis.
• ML-based bibliometric analysis is fundamental to detect unknown pattern of data in Radiomics publications. • A raising interest in the field, the most relevant collaborations, keywords co-occurrence network, and trending topics have been investigated. • Some pitfalls still exist, including the scarce standardization and the relative lack of homogeneity across studies. | European radiology | 2023-04-19T00:00:00 | [
"StefaniaVolpe",
"FedericoMastroleo",
"MarcoKrengli",
"Barbara AlicjaJereczek-Fossa"
] | 10.1007/s00330-023-09645-6
10.1016/j.ejca.2011.11.036
10.1038/s41598-021-01470-5
10.1007/s00330-021-08009-2
10.1038/s42003-021-02894-5
10.1080/0284186X.2021.1983207
10.3390/diagnostics12040794
10.3389/fonc.2018.00131
10.3389/fonc.2018.00294
10.1038/nrclinonc.2017.141
10.1016/j.jbusres.2021.04.070
10.1016/j.joi.2017.08.007
10.3390/su14063643
10.1038/s41598-020-69250-1
10.1148/radiol.2015151169
10.1038/ncomms5006
10.1148/radiol.2020191145
10.1002/asi.20317 |
Analysis of Covid-19 CT chest image classification using Dl4jMlp classifier and Multilayer Perceptron in WEKA Environment. | In recent years, various deep learning algorithms have exhibited remarkable performance in various data-rich applications, like health care, medical imaging, as well as in computer vision. Covid-19, which is a rapidly spreading virus, has affected people of all ages both socially and economically. Early detection of this virus is therefore important in order to prevent its further spread.
Covid-19 crisis has also galvanized researchers to adopt various machine learning as well as deep learning techniques in order to combat the pandemic. Lung images can be used in the diagnosis of Covid-19.
In this paper, we have analysed the Covid-19 chest CT image classification efficiency using multilayer perceptron with different imaging filters, like edge histogram filter, colour histogram equalization filter, color-layout filter, and Garbo filter in the WEKA environment.
The performance of CT image classification has also been compared comprehensively with the deep learning classifier Dl4jMlp. It was observed that the multilayer perceptron with edge histogram filter outperformed other classifiers compared in this paper with 89.6% of correctly classified instances. | Current medical imaging | 2023-04-19T00:00:00 | [
"NoneSreejith S",
"NoneJ Ajayan",
"NoneN V Uma Reddy",
"BabuDevasenapati S",
"ShashankRebelli"
] | 10.2174/1573405620666230417090246 |
CCS-GAN: COVID-19 CT Scan Generation and Classification with Very Few Positive Training Images. | We present a novel algorithm that is able to generate deep synthetic COVID-19 pneumonia CT scan slices using a very small sample of positive training images in tandem with a larger number of normal images. This generative algorithm produces images of sufficient accuracy to enable a DNN classifier to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic during the COVID-19 pandemic, because for quite some time it was difficult to obtain large volumes of COVID-19-positive images for training. Algorithms that can learn to screen for diseases using few examples are an important area of research. Furthermore, algorithms to produce deep synthetic images with smaller data volumes have the added benefit of reducing the barriers of data sharing between healthcare institutions. We present the cycle-consistent segmentation-generative adversarial network (CCS-GAN). CCS-GAN combines style transfer with pulmonary segmentation and relevant transfer learning from negative images in order to create a larger volume of synthetic positive images for the purposes of improving diagnostic classification performance. The performance of a VGG-19 classifier plus CCS-GAN was trained using a small sample of positive image slices ranging from at most 50 down to as few as 10 COVID-19-positive CT scan images. CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19. | Journal of digital imaging | 2023-04-18T00:00:00 | [
"SumeetMenon",
"JayalakshmiMangalagiri",
"JoshGalita",
"MichaelMorris",
"BabakSaboury",
"YaacovYesha",
"YelenaYesha",
"PhuongNguyen",
"AryyaGangopadhyay",
"DavidChapman"
] | 10.1007/s10278-023-00811-2
10.1007/s10489-020-01862-6
10.1109/CSCI51800.2020.00160
10.1016/j.eswa.2021.114848
10.1016/j.neucom.2018.09.013
10.1016/S0031-3203(02)00060-2 |
A lightweight CORONA-NET for COVID-19 detection in X-ray images. | Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors. | Expert systems with applications | 2023-04-18T00:00:00 | [
"Muhammad UsmanHadi",
"RizwanQureshi",
"AyeshaAhmed",
"NadeemIftikhar"
] | 10.1016/j.eswa.2023.120023 |
A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia. | The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.
LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.
Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.
Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.
We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia. | European radiology experimental | 2023-04-10T00:00:00 | [
"CamillaScapicchio",
"AndreaChincarini",
"ElenaBallante",
"LucaBerta",
"EleonoraBicci",
"ChandraBortolotto",
"FrancescaBrero",
"Raffaella FiammaCabini",
"GiuseppeCristofalo",
"Salvatore ClaudioFanni",
"Maria EvelinaFantacci",
"SilviaFigini",
"MassimoGalia",
"PietroGemma",
"EmanueleGrassedonio",
"AlessandroLascialfari",
"CristinaLenardi",
"AliceLionetti",
"FrancescaLizzi",
"MaurizioMarrale",
"MassimoMidiri",
"CosimoNardi",
"PiernicolaOliva",
"NoemiPerillo",
"IanPostuma",
"LorenzoPreda",
"VieriRastrelli",
"FrancescoRizzetto",
"NicolaSpina",
"CinziaTalamonti",
"AlbertoTorresin",
"AngeloVanzulli",
"FedericaVolpi",
"EmanueleNeri",
"AlessandraRetico"
] | 10.1186/s41747-023-00334-z
10.1007/s00330-020-07347-x
10.21037/atm-20-3311
10.1093/rheumatology/keab615
10.1016/j.ejrad.2021.109650
10.1148/radiol.2020200527
10.1007/s11547-020-01237-4
10.1097/RLI.0000000000000689
10.1016/j.ejmp.2021.01.004
10.1007/s10278-019-00223-1
10.1016/j.patcog.2021.108071
10.1016/j.ejmp.2021.06.001
10.1016/j.ejro.2020.100272
10.1016/j.radonc.2020.09.045
10.1038/srep23376
10.1007/s10140-020-01867-1
10.21037/qims-22-175
10.1007/s11548-021-02501-2
10.1148/ryai.2020200029
10.1148/radiol.2462070712
10.7150/ijms.50568
10.1016/j.jcm.2016.02.012
10.1016/j.nicl.2019.101846
10.1007/s11547-020-01291-y
10.1016/j.jinf.2020.02.017
10.1016/j.acra.2020.03.003
10.1016/j.ejrad.2020.109209
10.1120/jacmp.v16i4.5001
10.1016/j.chest.2021.06.063 |
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images. | Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method. | IEEE journal of biomedical and health informatics | 2023-04-08T00:00:00 | [
"ShengchaoChen",
"SufenRen",
"GuanjunWang",
"MengxingHuang",
"ChenyangXue"
] | 10.1109/JBHI.2023.3247949 |
Redefining Lobe-Wise Ground-Glass Opacity in COVID-19 Through Deep Learning and its Correlation With Biochemical Parameters. | During COVID-19 pandemic qRT-PCR, CT scans and biochemical parameters were studied to understand the patients' physiological changes and disease progression. There is a lack of clear understanding of the correlation of lung inflammation with biochemical parameters available. Among the 1136 patients studied, C-reactive-protein (CRP) is the most critical parameter for classifying symptomatic and asymptomatic groups. Elevated CRP is corroborated with increased D-dimer, Gamma-glutamyl-transferase (GGT), and urea levels in COVID-19 patients. To overcome the limitations of manual chest CT scoring system, we segmented the lungs and detected ground-glass-opacity (GGO) in specific lobes from 2D CT images by 2D U-Net-based deep learning (DL) approach. Our method shows accuracy, compared to the manual method ( ∼ 80%), which is subjected to the radiologist's experience. We determined a positive correlation of GGO in the right upper-middle (0.34) and lower (0.26) lobe with D-dimer. However, a modest correlation was observed with CRP, ferritin and other studied parameters. The final Dice Coefficient (or the F1 score) and Intersection-Over-Union for testing accuracy are 95.44% and 91.95%, respectively. This study can help reduce the burden and manual bias besides increasing the accuracy of GGO scoring. Further study on geographically diverse large populations may help to understand the association of the biochemical parameters and pattern of GGO in lung lobes with different SARS-CoV-2 Variants of Concern's disease pathogenesis in these populations. | IEEE journal of biomedical and health informatics | 2023-04-07T00:00:00 | [
"BudhadevBaral",
"KartikMuduli",
"ShwetaJakhmola",
"OmkarIndari",
"JatinJangir",
"Ashraf HaroonRashid",
"SuchitaJain",
"Amrut KumarMohapatra",
"ShubhransuPatro",
"PreetinandaParida",
"NamrataMisra",
"Ambika PrasadMohanty",
"Bikash RSahu",
"Ajay KumarJain",
"SelvakumarElangovan",
"Hamendra SinghParmar",
"MTanveer",
"Nirmal KumarMohakud",
"Hem ChandraJha"
] | 10.1109/JBHI.2023.3263431 |
Smart Artificial Intelligence techniques using embedded band for diagnosis and combating COVID-19. | Recently, COVID-19 virus spread to create a major impact in human body worldwide. The Corona virus, initiated by the SARS-CoV-2 virus, was known in China, December 2019 and affirmed a worldwide epidemic by the World Health Organization on 11 March 2020. The core aim of this research is to detect the spreading of COVID-19 virus and solve the problems in human lungs infection quickly. An Artificial Intelligence (AI) technique is a possibly controlling device in the battle against the corona virus epidemic. Recently, AI with computational techniques are utilized for COVID-19 virus with the building blocks of Deep Learning method using Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) is used to classify and identify the lung images affected region. These two algorithms used to diagnose COVID-19 infections rapidly. The AI applications against COVID-19 are Medical Imaging for Diagnosis, Lung delineation, Lesion measurement, Non-Invasive Measurements for Disease Tracking, Patient Outcome Prediction, Molecular Scale: from Proteins to Drug Development and Societal Scale: Epidemiology and Infodemiology. | Microprocessors and microsystems | 2023-04-06T00:00:00 | [
"MAshwin",
"Abdulrahman SaadAlqahtani",
"AzathMubarakali"
] | 10.1016/j.micpro.2023.104819
10.1109/rbme.2020.2987975
10.1186/s40537-020-00392-9 |
Federated Active Learning for Multicenter Collaborative Disease Diagnosis. | Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis. | IEEE transactions on medical imaging | 2023-04-05T00:00:00 | [
"XingWu",
"JiePei",
"ChengChen",
"YiminZhu",
"JianjiaWang",
"QuanQian",
"JianZhang",
"QunSun",
"YikeGuo"
] | 10.1109/TMI.2022.3227563 |
Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level. | Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients. | Ultrasonics | 2023-04-05T00:00:00 | [
"UmairKhan",
"SajjadAfrakhteh",
"FedericoMento",
"NoreenFatima",
"LauraDe Rosa",
"Leonardo LucioCustode",
"ZihadulAzam",
"ElenaTorri",
"GinoSoldati",
"FrancescoTursi",
"Veronica NarvenaMacioce",
"AndreaSmargiassi",
"RiccardoInchingolo",
"TizianoPerrone",
"GiovanniIacca",
"LibertarioDemi"
] | 10.1016/j.ultras.2023.106994
10.1038/d41586-022-00858-1 |
PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. | The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing. | Engineering applications of artificial intelligence | 2023-04-04T00:00:00 | [
"Farhan FuadAbir",
"Muhammad E HChowdhury",
"Malisha IslamTapotee",
"AdamMushtak",
"AmithKhandakar",
"SakibMahmud",
"Md AnwarulHasan"
] | 10.1016/j.engappai.2023.106130
10.48550/arXiv.1603.04467
10.1016/j.compbiomed.2022.105682
10.1038/s41591-021-01593-2
10.1016/j.compbiomed.2022.106070
10.1109/MPRV.2020.3021321
10.3390/jcm9103372
10.3390/biology9080182
10.1016/S2666-5247(20)30172-5
10.3390/s21175787
10.1038/s41598-022-11329-y
10.2217/pme-2018-0044
10.1056/NEJMe2009758
10.1371/journal.pone.0240123
10.1038/s41586-020-2649-2
10.48550/arXiv.1312.6114
10.3390/jimaging4020036
10.1056/NEJMoa2001316
10.1109/ICASSP40776.2020.9053558
10.48550/arXiv.2205.13607
10.1016/S2589-7500(22)00019-X
10.1117/1.JBO.25.10.102703
10.3389/fdgth.2020.00008
10.5281/zenodo.3509134 |
A COVID-19 medical image classification algorithm based on Transformer. | Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. On the backbone of ResNet-50, it uses Transformer to capture long-distance feature information, adopts convolutional neural networks and depth-wise convolution to obtain local features, reduce the computational cost and acceleration the detection process. The RMT-Net includes four stage blocks to realize the feature extraction of different receptive fields. In the first three stages, the global self-attention method is adopted to capture the important feature information and construct the relationship between tokens. In the fourth stage, the residual blocks are used to extract the details of feature. Finally, a global average pooling layer and a fully connected layer perform classification tasks. Training, verification and testing are carried out on self-built datasets. The RMT-Net model is compared with ResNet-50, VGGNet-16, i-CapsNet and MGMADS-3. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. The size of RMT-Net model is only 38.5 M, and the detection speed of X-ray image and CT image is 5.46 ms and 4.12 ms per image, respectively. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency. | Scientific reports | 2023-04-04T00:00:00 | [
"KeyingRen",
"GengHong",
"XiaoyanChen",
"ZichenWang"
] | 10.1038/s41598-023-32462-2
10.1016/j.compbiomed.2021.105134
10.1016/j.chaos.2020.110495
10.1148/radiol.2020200343
10.1148/radiol.2020200463
10.1016/j.compbiomed.2021.105123
10.1038/s41598-021-97428-8
10.1109/TCBB.2021.3065361
10.1016/j.patcog.2020.107747
10.1016/j.bspc.2021.103371
10.1016/j.irbm.2020.05.003
10.3390/jpm12020310
10.3390/jcm11113013
10.1007/s13042-022-01676-7
10.1016/j.cmpb.2022.107141
10.1109/TMI.2020.2995965
10.1007/s40846-020-00529-4
10.1007/s12559-020-09775-9
10.1007/s10489-020-01829-7
10.1016/j.asoc.2022.108780
10.1007/s13246-020-00865-4
10.1016/j.compbiomed.2020.103792
10.1016/j.compbiomed.2022.105244
10.1016/j.compbiomed.2021.104399
10.1016/j.cmpb.2020.105581
10.1016/j.bspc.2021.102588
10.3390/tomography8020071
10.1007/s10096-020-03901-z
10.3389/frai.2021.598932
10.1148/radiol.2020200905
10.1016/j.compbiomed.2020.104037 |
Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning. | Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberculosis can be fatal in some cases and lives could be saved through early detection and appropriate intervention for the advanced cases. Thus CXRs can be used for an automated severity grading of pulmonary diseases that can aid radiologists in making better and informed diagnoses. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique in which the amount of augmentations at each step is capped. Our base network in the framework is first trained using modified progressive learning and can then be tweaked for new data sets. Furthermore, the segmentation task makes use of an attention map generated within and by the network itself. This attention mechanism allows to achieve segmentation results that are on par with networks having an order of magnitude or more parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single-digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On the BRAX validation data set, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning, respectively. A mean matching score of 80.8% is obtained for severity score grading while an average area under receiver operating characteristic curve of 0.88 is achieved for classification. | Biomedical signal processing and control | 2023-03-30T00:00:00 | [
"Asad MansoorKhan",
"Muhammad UsmanAkram",
"SajidNazir",
"TaimurHassan",
"Sajid GulKhawaja",
"TatheerFatima"
] | 10.1016/j.bspc.2023.104855 |
Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier. | In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train-test splits (70-30%, 80-20%, and 90-10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease. | Healthcare (Basel, Switzerland) | 2023-03-30T00:00:00 | [
"KashifShaheed",
"PiotrSzczuko",
"QaisarAbbas",
"AyyazHussain",
"MubarakAlbathan"
] | 10.3390/healthcare11060837
10.1002/jmv.25678
10.1016/S0140-6736(21)02046-8
10.1016/S0140-6736(21)02249-2
10.1148/radiol.2020200230
10.1109/TMI.2020.3040950
10.1109/TMI.2020.2993291
10.1152/physiolgenomics.00029.2020
10.3390/ijerph19042013
10.1148/radiol.2020200527
10.1016/j.jcv.2020.104384
10.1016/S1473-3099(20)30086-4
10.1038/s41598-020-76550-z
10.1016/j.asoc.2022.109319
10.1016/j.compbiomed.2022.105233
10.1016/j.compbiomed.2020.103792
10.1109/TMI.2020.2996645
10.1007/s10489-020-01826-w
10.1007/s10044-021-00984-y
10.1016/j.compbiomed.2020.103795
10.3390/ijerph20032035
10.1101/2020.03.30.20047787
10.1016/j.compbiomed.2021.104781
10.1016/j.ipm.2022.103025
10.1016/j.neucom.2022.01.055
10.1016/j.radi.2022.03.011
10.1007/s00521-020-05017-z
10.3390/diagnostics12123109
10.1109/ICCV48922.2021.00009
10.1007/978-3-030-62008-0_35
10.1016/j.ijid.2020.06.058
10.1371/journal.pone.0096385
10.7717/peerj.5518
10.1007/978-3-540-74825-0_11
10.1136/bmjopen-2018-025925
10.1016/j.chaos.2020.109944
10.1177/2472630320962002
10.1007/s11042-020-09431-2
10.1109/ACCESS.2021.3058854
10.3991/ijoe.v18i07.30807 |
Comparative Analysis of Clinical and CT Findings in Patients with SARS-CoV-2 Original Strain, Delta and Omicron Variants. | To compare the clinical characteristics and chest CT findings of patients infected with Omicron and Delta variants and the original strain of COVID-19.
A total of 503 patients infected with the original strain (245 cases), Delta variant (90 cases), and Omicron variant (168 cases) were retrospectively analyzed. The differences in clinical severity and chest CT findings were analyzed. We also compared the infection severity of patients with different vaccination statuses and quantified pneumonia by a deep-learning approach.
The rate of severe disease decreased significantly from the original strain to the Delta variant and Omicron variant (27% vs. 10% vs. 4.8%,
Compared with the original strain and Delta variant, the Omicron variant had less clinical severity and less lung injury on CT scans. | Biomedicines | 2023-03-30T00:00:00 | [
"XiaoyuHan",
"JingzeChen",
"LuChen",
"XiJia",
"YanqingFan",
"YutingZheng",
"OsamahAlwalid",
"JieLiu",
"YuminLi",
"NaLi",
"JinGu",
"JiangtaoWang",
"HeshuiShi"
] | 10.3390/biomedicines11030901
10.1016/S0140-6736(20)30183-5
10.1016/S0140-6736(20)30185-9
10.1016/S0140-6736(21)00370-6
10.1038/s41579-021-00573-0
10.1016/S0140-6736(20)30211-7
10.1016/S1473-3099(20)30086-4
10.1016/S0140-6736(20)30566-3
10.1148/radiol.220533
10.1148/radiol.229022
10.1038/s41467-020-18685-1
10.1109/TMI.2020.2992546
10.1148/radiol.2020200823
10.15585/mmwr.mm7101a4
10.1038/s41467-022-28089-y
10.1080/22221751.2021.2022440
10.1148/ryct.2020200152
10.1148/radiol.2462070712
10.1148/radiol.2363040958
10.1016/S2589-7500(20)30199-0
10.1148/ryct.2020200075
10.1016/S0140-6736(22)00017-4
10.1001/jama.2022.2274
10.1016/S0140-6736(22)00462-7
10.1016/S1473-3099(21)00475-8
10.1503/cmaj.211248
10.1016/j.cell.2020.04.004
10.1038/s41598-020-74497-9
10.1001/jama.2021.24315
10.1136/bmj.n3144
10.2214/AJR.21.26560
10.1016/j.jacr.2020.06.006
10.1016/S0140-6736(21)02249-2
10.1038/s41422-022-00674-2
10.3390/jpm12060955 |
Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets. | Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses. | Bioengineering (Basel, Switzerland) | 2023-03-30T00:00:00 | [
"XingsiXue",
"SeelammalChinnaperumal",
"Ghaida MuttasharAbdulsahib",
"Rajasekhar ReddyManyam",
"RajaMarappan",
"Sekar KidambiRaju",
"Osamah IbrahimKhalaf"
] | 10.3390/bioengineering10030363
10.1007/s42979-021-00823-1
10.1016/j.compbiomed.2022.105344
10.1038/s42003-020-01535-7
10.1007/s11517-020-02299-2
10.1007/s10044-021-00970-4
10.31224/osf.io/wx89s
10.1038/s41598-021-99015-3
10.1007/s40846-021-00653-9
10.3390/cmim.2021.10008
10.1007/s12530-021-09385-2
10.1109/JIOT.2021.3056185
10.1007/s10489-020-01829-7
10.1016/j.imu.2020.100412
10.1007/s10489-020-01867-1
10.1155/2021/5513679
10.1109/ACCESS.2020.3016780
10.3390/s20236985
10.1007/s10489-021-02393-4
10.48084/etasr.4613
10.11591/ijece.v11i1.pp844-850
10.1007/s10522-021-09946-7
10.21817/indjcse/2021/v12i1/211201064
10.3390/s21175813
10.3390/diagnostics11020340.2021
10.1166/jctn.2020.9439
10.14704/WEB/V19I1/WEB19071
10.1016/j.chaos.2020.110120
10.1007/s10489-020-02055-x
10.1016/j.irbm.2020.05.003
10.1007/s40747-021-00509-4
10.1109/CIMSim.2013.17
10.1109/ICCIC.2013.6724190
10.1109/ICICES.2016.7518914
10.3390/telecom4010008c
10.1007/s13369-017-2686-9
10.3390/math8030303
10.3390/math8071106
10.3390/math9020197
10.3390/bioengineering10020138
10.1155/2022/9227343
10.32604/iasc.2022.025609
10.1155/2021/5574376
10.4018/IJRQEH.289176
10.1007/s13369-021-06323-x
10.1109/ICICES.2016.7518911
10.1109/ICCIC.2018.8782425
10.1007/s41870-023-01165-2 |
Perceptive SARS-CoV-2 End-To-End Ultrasound Video Classification through X3D and Key-Frames Selection. | The SARS-CoV-2 pandemic challenged health systems worldwide, thus advocating for practical, quick and highly trustworthy diagnostic instruments to help medical personnel. It features a long incubation period and a high contagion rate, causing bilateral multi-focal interstitial pneumonia, generally growing into acute respiratory distress syndrome (ARDS), causing hundreds of thousands of casualties worldwide. Guidelines for first-line diagnosis of pneumonia suggest Chest X-rays (CXR) for patients exhibiting symptoms. Potential alternatives include Computed Tomography (CT) scans and Lung UltraSound (LUS). Deep learning (DL) has been helpful in diagnosis using CT scans, LUS, and CXR, whereby the former commonly yields more precise results. CXR and CT scans present several drawbacks, including high costs. Radiation-free LUS imaging requires high expertise, and physicians thus underutilise it. LUS demonstrated a strong correlation with CT scans and reliability in pneumonia detection, even in the early stages. Here, we present an LUS video-classification approach based on contemporary DL strategies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. This research addressed SARS-CoV-2 patterns detection, ranked according to three severity scales by operating a trustworthy dataset comprising ultrasounds from linear and convex probes in 5400 clips from 450 hospitalised subjects. The main contributions of this study are related to the adoption of a standardised severity ranking scale to evaluate pneumonia. This evaluation relies on video summarisation through key-frame selection algorithms. Then, we designed and developed a video-classification architecture which emerged as the most promising. In contrast, the literature primarily concentrates on frame-pattern recognition. By using advanced techniques such as transfer learning and data augmentation, we were able to achieve an F1-Score of over 89% across all classes. | Bioengineering (Basel, Switzerland) | 2023-03-30T00:00:00 | [
"MarcoGazzoni",
"MarcoLa Salvia",
"EmanueleTorti",
"GianmarcoSecco",
"StefanoPerlini",
"FrancescoLeporati"
] | 10.3390/bioengineering10030282
10.1056/NEJMoa2001316
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GW- CNNDC: Gradient weighted CNN model for diagnosing COVID-19 using radiography X-ray images. | COVID-19 is one of the dangerous viruses that cause death if the patient doesn't identify it in the early stages. Firstly, this virus is identified in China, Wuhan city. This virus spreads very fast compared with other viruses. Many tests are there for detecting this virus, and also side effects may find while testing this disease. Corona-virus tests are now rare; there are restricted COVID-19 testing units and they can't be made quickly enough, causing alarm. Thus, we want to depend on other determination measures. There are three distinct sorts of COVID-19 testing systems: RTPCR, CT, and CXR. There are certain limitations to RTPCR, which is the most time-consuming technique, and CT-scan results in exposure to radiation which may cause further diseases. So, to overcome these limitations, the CXR technique emits comparatively less radiation, and the patient need not be close to the medical staff. COVID-19 detection from CXR images has been tested using a diversity of pre-trained deep-learning algorithms, with the best methods being fine-tuned to maximize detection accuracy. In this work, the model called GW-CNNDC is presented. The Lung Radiography pictures are portioned utilizing the Enhanced CNN model, deployed with RESNET-50 Architecture with an image size of 255*255 pixels. Afterward, the Gradient Weighted model is applied, which shows the specific separation regardless of whether the individual is impacted by Covid-19 affected area. This framework can perform twofold class assignments with exactness and accuracy, precision, recall, F1-score, and Loss value, and the model turns out proficiently for huge datasets with less measure of time. | Measurement. Sensors | 2023-03-28T00:00:00 | [
"PamulaUdayaraju",
"T VenkataNarayana",
"Sri HarshaVemparala",
"ChopparapuSrinivasarao",
"BhV S R KRaju"
] | 10.1016/j.measen.2023.100735
10.1007/s12098-020
10.1109/TII.2021.3057683
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10.1109/JAS.2020.1003393
10.1109/TMI.2020.2994459 |
Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images. | Hundreds of millions of people worldwide have recently been infected by the novel Coronavirus disease (COVID-19), causing significant damage to the health, economy, and welfare of the world's population. Moreover, the unprecedented number of patients with COVID-19 has placed a massive burden on healthcare centers, making timely and rapid diagnosis challenging. A crucial step in minimizing the impact of such problems is to automatically detect infected patients and place them under special care as quickly as possible. Deep learning algorithms, such as Convolutional Neural Networks (CNN), can be used to meet this need. Despite the desired results, most of the existing deep learning-based models were built on millions of parameters (weights), which are not applicable to devices with limited resources. Inspired by such fact, in this research, we developed two new lightweight CNN-based diagnostic models for the automatic and early detection of COVID-19 subjects from chest X-ray images. The first model was built for binary classification (COVID-19 and Normal), whereas the second one was built for multiclass classification (COVID-19, viral pneumonia, or normal). The proposed models were tested on a relatively large dataset of chest X-ray images, and the results showed that the accuracy rates of the 2- and 3-class-based classification models are 98.55% and 96.83%, respectively. The results also revealed that our models achieved competitive performance compared with the existing heavyweight models while significantly reducing cost and memory requirements for computing resources. With these findings, we can indicate that our models are helpful to clinicians in making insightful diagnoses of COVID-19 and are potentially easily deployable on devices with limited computational power and resources. | Expert systems with applications | 2023-03-28T00:00:00 | [
"Haval IHussein",
"Abdulhakeem OMohammed",
"Masoud MHassan",
"Ramadhan JMstafa"
] | 10.1016/j.eswa.2023.119900
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10.1007/s10489-020-01867-1 |
MTMC-AUR2CNet: Multi-textural multi-class attention recurrent residual convolutional neural network for COVID-19 classification using chest X-ray images. | Coronavirus disease (COVID-19) has infected over 603 million confirmed cases as of September 2022, and its rapid spread has raised concerns worldwide. More than 6.4 million fatalities in confirmed patients have been reported. According to reports, the COVID-19 virus causes lung damage and rapidly mutates before the patient receives any diagnosis-specific medicine. Daily increasing COVID-19 cases and the limited number of diagnosis tool kits encourage the use of deep learning (DL) models to assist health care practitioners using chest X-ray (CXR) images. The CXR is a low radiation radiography tool available in hospitals to diagnose COVID-19 and combat this spread. We propose a Multi-Textural Multi-Class (MTMC) UNet-based Recurrent Residual Convolutional Neural Network (MTMC-UR2CNet) and MTMC-UR2CNet with attention mechanism (MTMC-AUR2CNet) for multi-class lung lobe segmentation of CXR images. The lung lobe segmentation output of MTMC-UR2CNet and MTMC-AUR2CNet are mapped individually with their input CXRs to generate the region of interest (ROI). The multi-textural features are extracted from the ROI of each proposed MTMC network. The extracted multi-textural features from ROI are fused and are trained to the Whale optimization algorithm (WOA) based DeepCNN classifier on classifying the CXR images into normal (healthy), COVID-19, viral pneumonia, and lung opacity. The experimental result shows that the MTMC-AUR2CNet has superior performance in multi-class lung lobe segmentation of CXR images with an accuracy of 99.47%, followed by MTMC-UR2CNet with an accuracy of 98.39%. Also, MTMC-AUR2CNet improves the multi-textural multi-class classification accuracy of the WOA-based DeepCNN classifier to 97.60% compared to MTMC-UR2CNet. | Biomedical signal processing and control | 2023-03-28T00:00:00 | [
"AnandbabuGopatoti",
"PVijayalakshmi"
] | 10.1016/j.bspc.2023.104857
10.1109/TMI.2018.2806086
10.1016/j.bspc.2022.103860 |
PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans. | Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been widely used to analyze this disease. Indeed, CT-scans of the lungs can help diagnose, detect, and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. To improve the performance of the Att-Unet architecture and maximize the use of the Attention Gate, we propose the PAtt-Unet and DAtt-Unet architectures. PAtt-Unet aims to exploit the input pyramids to preserve the spatial awareness in all of the encoder layers. On the other hand, DAtt-Unet is designed to guide the segmentation of Covid-19 infection inside the lung lobes. We also propose to combine these two architectures into a single one, which we refer to as PDAtt-Unet. To overcome the blurry boundary pixels segmentation of Covid-19 infection, we propose a hybrid loss function. The proposed architectures were tested on four datasets with two evaluation scenarios (intra and cross datasets). Experimental results showed that both PAtt-Unet and DAtt-Unet improve the performance of Att-Unet in segmenting Covid-19 infections. Moreover, the combination architecture PDAtt-Unet led to further improvement. To Compare with other methods, three baseline segmentation architectures (Unet, Unet++, and Att-Unet) and three state-of-the-art architectures (InfNet, SCOATNet, and nCoVSegNet) were tested. The comparison showed the superiority of the proposed PDAtt-Unet trained with the proposed hybrid loss (PDEAtt-Unet) over all other methods. Moreover, PDEAtt-Unet is able to overcome various challenges in segmenting Covid-19 infections in four datasets and two evaluation scenarios. | Medical image analysis | 2023-03-27T00:00:00 | [
"FaresBougourzi",
"CosimoDistante",
"FadiDornaika",
"AbdelmalikTaleb-Ahmed"
] | 10.1016/j.media.2023.102797
10.1016/j.knosys.2020.106647
10.3390/s21175878
10.3390/jimaging7090189
10.1016/j.eswa.2020.113459
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10.1109/TMI.2020.2996645
10.1186/s12967-021-02992-2
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10.7326/M20-1495
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10.1109/TNNLS.2021.3126305
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10.1016/j.media.2021.101992
10.1016/j.cell.2020.04.045
10.1016/j.compbiomed.2021.104526 |
Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models. | Animal modeling of infectious diseases such as coronavirus disease 2019 (COVID-19) is important for exploration of natural history, understanding of pathogenesis, and evaluation of countermeasures. Preclinical studies enable rigorous control of experimental conditions as well as pre-exposure baseline and longitudinal measurements, including medical imaging, that are often unavailable in the clinical research setting. Computerized tomography (CT) imaging provides important diagnostic, prognostic, and disease characterization to clinicians and clinical researchers. In that context, automated deep-learning systems for the analysis of CT imaging have been broadly proposed, but their practical utility has been limited. Manual outlining of the ground truth (i.e., lung-lesions) requires accurate distinctions between abnormal and normal tissues that often have vague boundaries and is subject to reader heterogeneity in interpretation. Indeed, this subjectivity is demonstrated as wide inconsistency in manual outlines among experts and from the same expert. The application of deep-learning data-science tools has been less well-evaluated in the preclinical setting, including in nonhuman primate (NHP) models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection/COVID-19, in which the translation of human-derived deep-learning tools is challenging. The automated segmentation of the whole lung and lung lesions provides a potentially standardized and automated method to detect and quantify disease.
We used deep-learning-based quantification of the whole lung and lung lesions on CT scans of NHPs exposed to SARS-CoV-2. We proposed a novel multi-model ensemble technique to address the inconsistency in the ground truths for deep-learning-based automated segmentation of the whole lung and lung lesions. Multiple models were obtained by training the convolutional neural network (CNN) on different subsets of the training data instead of having a single model using the entire training dataset. Moreover, we employed a feature pyramid network (FPN), a CNN that provides predictions at different resolution levels, enabling the network to predict objects with wide size variations.
We achieved an average of 99.4 and 60.2% Dice coefficients for whole-lung and lung-lesion segmentation, respectively. The proposed multi-model FPN outperformed well-accepted methods U-Net (50.5%), V-Net (54.5%), and Inception (53.4%) for the challenging lesion-segmentation task. We show the application of segmentation outputs for longitudinal quantification of lung disease in SARS-CoV-2-exposed and mock-exposed NHPs.
Deep-learning methods should be optimally characterized for and targeted specifically to preclinical research needs in terms of impact, automation, and dynamic quantification independently from purely clinical applications. | Academic radiology | 2023-03-26T00:00:00 | [
"Syed M SReza",
"Winston TChu",
"FatemehHomayounieh",
"MaximBlain",
"Fatemeh DFirouzabadi",
"Pouria YAnari",
"Ji HyunLee",
"GabriellaWorwa",
"Courtney LFinch",
"Jens HKuhn",
"AshkanMalayeri",
"IanCrozier",
"Bradford JWood",
"Irwin MFeuerstein",
"JeffreySolomon"
] | 10.1016/j.acra.2023.02.027
10.1038/s41586-020-2787-6
10.1038/s41572-020-0147-3
10.3390/pathogens9030197
10.1002/path.4444
10.48550/arXiv.2004.1285220
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10.1117/12.2607154
10.1007/s00134-020-06033-2
10.48550/arXiv.2003.04655
10.1109/CBMS.2014.59
10.1007/978-3-030-59861-7_58 |
MESTrans: Multi-scale embedding spatial transformer for medical image segmentation. | Transformers profiting from global information modeling derived from the self-attention mechanism have recently achieved remarkable performance in computer vision. In this study, a novel transformer-based medical image segmentation network called the multi-scale embedding spatial transformer (MESTrans) was proposed for medical image segmentation.
First, a dataset called COVID-DS36 was created from 4369 computed tomography (CT) images of 36 patients from a partner hospital, of which 18 had COVID-19 and 18 did not. Subsequently, a novel medical image segmentation network was proposed, which introduced a self-attention mechanism to improve the inherent limitation of convolutional neural networks (CNNs) and was capable of adaptively extracting discriminative information in both global and local content. Specifically, based on U-Net, a multi-scale embedding block (MEB) and multi-layer spatial attention transformer (SATrans) structure were designed, which can dynamically adjust the receptive field in accordance with the input content. The spatial relationship between multi-level and multi-scale image patches was modeled, and the global context information was captured effectively. To make the network concentrate on the salient feature region, a feature fusion module (FFM) was established, which performed global learning and soft selection between shallow and deep features, adaptively combining the encoder and decoder features. Four datasets comprising CT images, magnetic resonance (MR) images, and H&E-stained slide images were used to assess the performance of the proposed network.
Experiments were performed using four different types of medical image datasets. For the COVID-DS36 dataset, our method achieved a Dice similarity coefficient (DSC) of 81.23%. For the GlaS dataset, 89.95% DSC and 82.39% intersection over union (IoU) were obtained. On the Synapse dataset, the average DSC was 77.48% and the average Hausdorff distance (HD) was 31.69 mm. For the I2CVB dataset, 92.3% DSC and 85.8% IoU were obtained.
The experimental results demonstrate that the proposed model has an excellent generalization ability and outperforms other state-of-the-art methods. It is expected to be a potent tool to assist clinicians in auxiliary diagnosis and to promote the development of medical intelligence technology. | Computer methods and programs in biomedicine | 2023-03-26T00:00:00 | [
"YatongLiu",
"YuZhu",
"YingXin",
"YananZhang",
"DaweiYang",
"TaoXu"
] | 10.1016/j.cmpb.2023.107493 |
COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm. | Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis. | Results in engineering | 2023-03-23T00:00:00 | [
"Rahul GowthamPoola",
"LahariPl",
"Siva SankarY"
] | 10.1016/j.rineng.2023.101020
10.1016/j.catena.2019.104426
10.1021/acs.molpharmaceut.7b00578
10.1109/TMI.2020.3040950
10.1016/j.irbm.2020.05.003
10.1007/s13089-009-0003-x
10.1007/s10096-020-03901-z
10.1007/s10489-020-01829-7
10.1109/ACCESS.2020.3010287
10.1080/07391102.2020.1767212 |
COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks. | X-ray is a useful imaging modality widely utilized for diagnosing COVID-19 virus that infected a high number of people all around the world. The manual examination of these X-ray images may cause problems especially when there is lack of medical staff. Usage of deep learning models is known to be helpful for automated diagnosis of COVID-19 from the X-ray images. However, the widely used convolutional neural network architectures typically have many layers causing them to be computationally expensive. To address these problems, this study aims to design a lightweight differential diagnosis model based on convolutional neural networks. The proposed model is designed to classify the X-ray images belonging to one of the four classes that are Healthy, COVID-19, viral pneumonia, and bacterial pneumonia. To evaluate the model performance, accuracy, precision, recall, and F1-Score were calculated. The performance of the proposed model was compared with those obtained by applying transfer learning to the widely used convolutional neural network models. The results showed that the proposed model with low number of computational layers outperforms the pre-trained benchmark models, achieving an accuracy value of 89.89% while the best pre-trained model (Efficient-Net B2) achieved accuracy of 85.7%. In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy. | Network modeling and analysis in health informatics and bioinformatics | 2023-03-21T00:00:00 | [
"MuhabHariri",
"ErcanAvşar"
] | 10.1007/s13721-023-00413-6
10.31803//tg-20210422205610
10.1016/j.bspc.2020.102257
10.1109/ACCESS.2020.3010287
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10.1109/ACCESS.2021.3078241
10.1016/j.bspc.2022.103677
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10.3390/app12136364
10.1109/JAS.2020.1003393
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10.1007/s11263-015-0816-y
10.31803/tg-20190712095507
10.1016/j.asoc.2021.107522
10.3390/ijerph182111086
10.1109/ACCESS.2019.2892795
10.1016/j.micinf.2020.05.016 |
Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling. | There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.
A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases.
The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists.
This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19. | Frontiers in cellular and infection microbiology | 2023-03-21T00:00:00 | [
"Hong-TaoZhang",
"Ze-YuSun",
"JuanZhou",
"ShenGao",
"Jing-HuiDong",
"YuanLiu",
"XuBai",
"Jin-LinMa",
"MingLi",
"GuangLi",
"Jian-MingCai",
"Fu-GengSheng"
] | 10.3389/fcimb.2023.1116285
10.1148/radiol.2020200642
10.1118/1.3528204
10.1016/j.jacr.2020.03.006
10.1016/j.compbiomed.2022.106439
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10.1148/radiol.2021211583
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10.1109/EMBC.2019.8856972
10.1002/mp.16217
10.1007/s00330-020-06801-0
10.1088/1741-2552/acb089 |
Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images. | The novel coronavirus disease (COVID-19), which appeared in Wuhan, China, is spreading rapidly worldwide. Health systems in many countries have collapsed as a result of this pandemic, and hundreds of thousands of people have died due to acute respiratory distress syndrome caused by this virus. As a result, diagnosing COVID-19 in the early stages of infection is critical in the fight against the disease because it saves the patient's life and prevents the disease from spreading. In this study, we proposed a novel approach based on transfer learning and deep support vector data description (DSVDD) to distinguish among COVID-19, non-COVID-19 pneumonia, and intact CT images. Our approach consists of three models, each of which can classify one specific category as normal and the other as anomalous. To our knowledge, this is the first study to use the one-class DSVDD and transfer learning to diagnose lung disease. For the proposed approach, we used two scenarios: one with pretrained VGG16 and one with ResNet50. The proposed models were trained using data gathered with the assistance of an expert radiologist from three internet-accessible sources in end-to-end fusion using three split data ratios. Based on training with 70%, 50%, and 30% of the data, the proposed VGG16 models achieved (0.8281, 0.9170, and 0.9294) for the F1 score, while the proposed ResNet50 models achieved (0.9109, 0.9188, and 0.9333). | Computational intelligence and neuroscience | 2023-03-18T00:00:00 | [
"Akram AAlhadad",
"Reham RMostafa",
"Hazem MEl-Bakry"
] | 10.1155/2023/6070970
10.1056/nejmoa2001017
10.1016/s0140-6736(20)30183-5
10.32604/cmc.2022.024193
10.1016/j.ijid.2020.01.009
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10.1155/2021/2158184
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10.1016/j.bspc.2021.102588
10.1016/j.media.2017.07.005
10.3390/electronics9091439
10.1016/j.cmpb.2020.105581
10.1016/j.compbiomed.2022.105233
10.1146/annurev-bioeng-071516-044442
10.1016/j.ejrad.2020.109041
10.1016/j.compbiomed.2020.103795
10.1016/j.compbiomed.2020.104037
10.1109/tip.2021.3058783
10.1371/journal.pone.0257119
10.1109/tmi.2020.2994908
10.1109/access.2020.3005510
10.1007/s00330-021-07715-1
10.1016/j.asoc.2020.106885
10.1007/s13246-020-00865-4
10.1109/tmi.2020.2996256
10.1038/s41598-021-03287-8
10.1007/s10916-022-01868-2
10.3390/s21020455 |
CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers. | COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works. | Frontiers in public health | 2023-03-17T00:00:00 | [
"AbdolrezaMarefat",
"MahdiehMarefat",
"JavadHassannataj Joloudari",
"Mohammad AliNematollahi",
"RezaLashgari"
] | 10.3389/fpubh.2023.1025746
10.1007/s00405-020-06319-7
10.1007/s00415-020-10067-3
10.3390/v12040372
10.1016/j.eswa.2020.114054
10.1016/j.mlwa.2021.100134
10.1145/3065386
10.1007/s13244-018-0639-9
10.1016/j.neucom.2016.12.038
10.3390/s20020342
10.1186/s12880-022-00793-7
10.1016/j.asoc.2018.05.018
10.18653/v1/2020.emnlp-demos.6
10.18653/v1/D18-1045
10.1002/widm.1412
10.1007/s11432-018-9941-6
10.1145/3505244
10.1016/j.media.2017.07.005
10.1038/s41598-020-76550-z
10.1016/j.asoc.2020.106691
10.1007/s11517-020-02299-2
10.1016/j.imu.2020.100412
10.1007/s10044-021-00984-y
10.1007/s10489-020-01904-z
10.3390/jpm12020310
10.1109/JTEHM.2021.3134096
10.1109/ACCESS.2021.3058854
10.1007/s00264-020-04609-7
10.1016/j.compbiomed.2020.103792
10.1016/j.chaos.2020.109944
10.1007/s10489-020-01826-w
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10.1038/s41598-021-93543-8
10.1016/j.patcog.2021.108255
10.1016/j.compbiomed.2022.106483
10.5555/3295222.3295349
10.3389/fcvm.2021.760178
10.1109/TMI.2020.2995965
10.1148/radiol.2020201473
10.1016/j.chaos.2020.110120
10.1016/j.ipm.2022.103025
10.1016/j.bspc.2022.103848
10.36548/jismac.2021.2.006
10.1016/j.radi.2022.03.011
10.1016/j.bbe.2020.08.008
10.32604/cmc.2021.012955
10.1016/j.compbiomed.2020.103795
10.3390/diagnostics11101887
10.1007/s11042-022-12156-z
10.1002/cpe.6747 |
IRCM-Caps: An X-ray image detection method for COVID-19. | COVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet.
The proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal.
The test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively.
The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset. | The clinical respiratory journal | 2023-03-17T00:00:00 | [
"ShuoQiu",
"JinlinMa",
"ZipingMa"
] | 10.1111/crj.13599
10.1016/j.eswa.2019.01.048
10.1016/j.imu.2020.100360
10.1016/j.patrec.2020.09.010
10.1016/j.chaos.2021.110713
10.1016/j.chaos.2020.110122
10.1002/ima.22566
10.1016/j.compbiomed.2021.104399
10.1016/j.knosys.2020.105542
10.5815/ijigsp.2020.02.04
10.3389/frai.2021.598932
10.1016/j.measurement.2021.110289
10.1101/2020.03.12.20027185
10.1007/s10044-021-00984-y
10.1038/s41598-020-76550-z
10.1183/13993003.00775-2020
10.1016/j.bspc.2021.103272
10.1016/j.bspc.2022.104268
10.1016/j.bbe.2022.11.003
10.1016/j.eswa.2022.118576
10.1016/j.compeleceng.2022.108479 |
Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19. | Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19.
Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence.
A total of 385 eyes from 195 screening participants were included (mean age 52.43 ± 14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (
Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis. | Journal of telemedicine and telecare | 2023-03-14T00:00:00 | [
"ArethaZhu",
"PriyaTailor",
"RashikaVerma",
"IsisZhang",
"BrianSchott",
"CatherineYe",
"BernardSzirth",
"MiriamHabiel",
"Albert SKhouri"
] | 10.1177/1357633X231158832 |
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COVID-19 and SARS-CoV-2 Research Papers Dataset
The COVID-19 and SARS-CoV-2 Research Papers Dataset is a collection of research papers related to COVID-19 and the SARS-CoV-2 virus. This dataset provides information such as paper titles, abstracts, journals, publication dates, authors, and DOIs.
Dataset Details
- Dataset Name: COVID-19 and SARS-CoV-2 Research Papers Dataset
- Dataset Size: 2,477,946 bytes
- Number of Examples: 1,035
- Download Size: 1,290,257 bytes
- License: Apache-2.0
Dataset Fields
The dataset contains the following fields:
- Title: The title of the research paper. (dtype: string)
- Abstract: The abstract of the research paper. (dtype: string)
- Journal: The journal in which the research paper was published. (dtype: string)
- Date: The publication date of the research paper. (dtype: timestamp[s])
- Authors: The authors of the research paper. (sequence: string)
- DOI: The Digital Object Identifier (DOI) of the research paper. (dtype: string)
Dataset Splits
The dataset is split into the following:
- Train: This split contains 1,035 examples from the COVID-19 and SARS-CoV-2 research papers.
Usage
You can use the COVID-19 and SARS-CoV-2 Research Papers Dataset for various purposes, including text mining, natural language processing (NLP) tasks, and analyzing research trends in the context of COVID-19.
Loading the Dataset
To load the COVID-19 and SARS-CoV-2 Research Papers Dataset, you can use the Hugging Face Datasets library. Here's an example:
from datasets import load_dataset
# Load the dataset
dataset = load_dataset('pt-sk/ml_research_abs')
# Access the fields
titles = dataset['train']['title']
abstracts = dataset['train']['abstract']
journals = dataset['train']['journal']
dates = dataset['train']['date']
authors = dataset['train']['authors']
dois = dataset['train']['doi']
Citation
If you use this dataset in your research or any other work, please consider citing it as:
@dataset{falah_covid19_sarscov2_research_dataset,
author = {Falah.G.Salieh},
title = {COVID-19 and SARS-CoV-2 Research Papers Dataset},
year = {2023},
publisher = {Hugging Face},
version = {1.0},
location = {Online},
url = {https://huggingface.co/datasets/pt-sk/ml_research_abs}
}
License
The COVID-19 and SARS-CoV-2 Research Papers Dataset is provided under the Apache-2.0 license.
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