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COVID-19 Radiography Classification and Visualization

This project aims to build a deep learning model for classifying and visualizing COVID-19 radiography images. It utilizes the VGG19 model with a Convolutional Block Attention Module (CBAM) to extract features from images and employs Grad-CAM for visualizing model predictions.

Key Features

  • Classification of COVID-19 radiography images
  • Visualization of model predictions using Grad-CAM
  • Application of various image filtering techniques
  • Model evaluation through K-fold cross-validation
  • Experiment tracking with Weights & Biases

Installation and Setup

Requirements

  • Python 3.x
  • PyTorch
  • torchvision
  • wandb
  • numpy
  • matplotlib
  • opencv-python

  1. Clone this repository:

    git clone https://github.com/yourusername/COVID19-Radiography-Project.git
cd COVID19-Radiography-Project

  2. Set up and activate a virtual environment:

    python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

  3. Install the required packages:

    pip install -r requirements.txt

Usage

  1. Run train_test_main.py to train the model:

    python train_test_main.py

  2. Run grad_cam_plot_main.py to perform Grad-CAM visualization:

    python grad_cam_plot_main.py

  3. Run kfold_main.py to perform K-fold cross-validation:

    python kfold_main.py

Contributors

License

This project is licensed under the MIT License. See the LICENSE file for details.