Detection and localization of COVID 19 and Pneumonia on chest radiographs

Overview

This repository contains the implementation of deep learning techniques for detecting and localizing COVID-19 lung lesions on chest radiographs. We utilized a multi-class classification approach with three deep learning architectures: DenseNet169, VGG16, and a custom sequential model.

Key Points

Objective: Early diagnosis of COVID-19 using deep learning algorithms on chest X-rays.
Models Used: DenseNet169, VGG16, and a non-pretrained sequential architecture.
Techniques: Transfer learning and ensemble learning were employed to classify radiographs into three categories: "COVID", "Pneumonia", and "Normal".
Database: The dataset consists of 3225 chest radiographs selected by a radiologist from the COVIDx-CXR version 8 database.
Results: Achieved state-of-the-art results with accuracy values above 83% for individual models and up to 96% for ensemble models.
Visualization: Class activation mapping (CAM) techniques were used to localize and visualize COVID lesions on chest radiographs.

Getting Started

To get started with the code, please follow the instructions below.

Prerequisites

Python 3.x
TensorFlow
Keras
OpenCV
NumPy
Matplotlib

Dataset

Available on : https://doi.org/10.6084/m9.figshare.25917340.v1

Models

Available on : https://drive.google.com/drive/folders/1RI_BU9Ew6b_HtK1wljF4Vt_Ug6dW2Jem?usp=drive_link

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

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

Contact

For any questions, please contact [ahmed.balaazi@etudiant-fmt.utm.tn]

AhmedBALAAZI/Detection-and-localization-of-COVID-19-and-Pneumonia-on-chest-radiographs