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--- |
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library_name: monai |
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tags: |
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- classification |
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- medical |
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- neuroscience |
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- alzheimer |
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license: mit |
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metrics: |
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- accuracy |
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pipeline_tag: image-classification |
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datasets: |
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- ADNI |
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--- |
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--- |
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### Model Description |
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A machine learning model for waste classification |
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- **Developed by:** rootstrap |
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- **Model type:** image-classifier |
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- **License:** mit |
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## Waste Classifier Model |
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The aim is to build a model for MRI classification that identifies among the different classes: |
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- Alzheimer's |
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- Mild Cognitive Impairment |
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- Control |
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This machine learning model will help medical staff get a second opinion on whether a pacient MRI indicates the presence of Alzheimer's desease |
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The model was built using **Monai** MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. |
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It has two main design goals: |
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To be approachable and rapidly productive |
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To be also configurable. |
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### Model Sources |
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- **Repository:** [https://github.com/rootstrap/MRI-classifier](https://github.com/rootstrap/MRI-classifier) |
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## Uses |
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At Rootstrap we classify waste. We found that people were struggled to classify correctly, |
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and then we end up not recycling most of the generated waste at the office, since if there were items in the wrong basket, |
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all the basket should not be classified. |
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Because of this reason, we created an app to help people at our company to classify waste. |
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### Direct Use |
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```bash |
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model = nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=3) |
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checkpoint = torch.load("86_acc_model.pth") |
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model.load_state_dict(checkpoint) |
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model.predict() |
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``` |
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## Bias, Risks, and Limitations |
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This model has an 86% of accuracy. Although it is an outstanding result, his means that 13% of times the prediction is wrong. |
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This is why its important to understand that this tool is not a real medical opinion and can not be used as a final diagnosis by any means. |
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This project does not aim to replace medical staff in diagnosing Alzheimer's desease, instead it is a tool to help them to get a quick and accurate second opinion. |
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## Training Details |
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### Training Data |
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The MRI data was gathered from the Alzheimer’s Desease Neuroimaging Initiative (ADNI) Database. |
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Afterwards, the data was splitted into one folder for each class. |
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Split into train/test |
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The data was splitted using the 60% for training, 20% for validation and 20% for testing |
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The model has been trained to classify waste into 3 classes. |
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The dataset used for the training consisted of 2527 images: |
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- 501 glass |
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- 594 paper |
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- 403 cardboard |
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- 482 plastic |
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- 410 metal |
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- 137 trash |
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### Training Procedure |
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You can find the code for training at [train.ipynb](https://github.com/rootstrap/MRI-classifier/blob/main/train.ipynb) |
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Training the model using DenseNet121 adapted for 3D images. |
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## Evaluation and Results |
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After 100 epochs, the model reached an accuracy of 85.76% |
