---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- medical
- covid
- covid19
- xray
---


# COVID-19 Detection using VGG19 and X-ray Images

## Overview

This model is able detect COVID-19 from X-ray images using the VGG19 architecture for transfer learning. The dataset used for this project is the COVID-19 Radiography Database available on Kaggle.

## Dataset

The dataset used in this project is the [COVID-19 Radiography Database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database). It contains X-ray images categorized into three classes: COVID, Normal, and other pneumonia. The dataset is split into training, validation, and test sets to ensure robust evaluation of the model.

## Methodology

### 1. Import Libraries

We start by importing the necessary libraries required for data processing, model building, and evaluation. These include TensorFlow for deep learning, matplotlib for visualization, and other essential packages.

### 2. Load Dataset

The dataset is loaded from the specified directory. This dataset contains X-ray images categorized into COVID, Normal, and other pneumonia classes. The images are stored in respective folders, which are read and preprocessed.

### 3. Data Preprocessing

- **Data Augmentation:** To increase the diversity of our training data, various transformations such as rotation, zoom, and horizontal flip are applied. This helps in making the model robust and prevents overfitting.
- **Rescaling:** The pixel values are rescaled to the range [0, 1] to standardize the input data, which improves model performance.

### 4. Split Dataset

The dataset is split into training, validation, and test sets. This is crucial for evaluating the model's performance on unseen data.
- **Training Set:** Used to train the model.
- **Validation Set:** Used to tune hyperparameters and prevent overfitting.
- **Test Set:** Used to assess the final model's performance.

### 5. Build the Model using VGG19

- **Transfer Learning:** The pre-trained VGG19 model, which has been trained on a large dataset (ImageNet), is used to leverage the learned features from a different domain to our specific task of COVID-19 detection.
- **Model Architecture:** Custom layers are added on top of VGG19 to adapt it to our classification problem. This includes flattening the output, adding dense layers, and a final softmax layer for classification.

### 6. Compile the Model

- **Loss Function:** 'binary_crossentropy' is used as the loss function because we have more than two classes.
- **Optimizer:** The Adam optimizer is used to adjust the learning rate dynamically.
- **Metrics:** Accuracy is tracked to monitor the performance of the model.

### 7. Train the Model

- **Epochs:** The number of times the entire training dataset is passed forward and backward through the neural network.
- **Batch Size:** The number of training examples utilized in one iteration.
- **Validation Data:** Helps in monitoring the model's performance on unseen data during training to tune hyperparameters and avoid overfitting.

### 8. Evaluate the Model

The model is evaluated on the test set to determine its accuracy, precision, recall, and F1 score. This helps in understanding the model's performance comprehensively.

### 9. Visualize Training Results

- **Loss and Accuracy Plots:** Visualize the training and validation loss and accuracy to understand how well the model is learning and if it's overfitting or underfitting.
- **Confusion Matrix:** Provides a detailed breakdown of true positives, false positives, true negatives, and false negatives, giving insights into where the model is making errors.

### 10. Conclusion

The findings and the performance of the model are summarized. Potential improvements or future work such as experimenting with different architectures, more data, or advanced preprocessing techniques are discussed.

## Results

The model achieves an accuracy of 98.1% on the test set, indicating its effectiveness in detecting COVID-19 from X-ray images. The high accuracy demonstrates the successful application of data preprocessing, augmentation, and model training techniques.

## Acknowledgements

- [COVID-19 Radiography Database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database)
- [VGG19 Model](https://arxiv.org/abs/1409.1556)