Chest X-ray Disease Multi-Classification

This project aims to classify chest X-ray images into multiple categories of diseases using deep learning models. The project utilizes the YOLO model for object detection and the DenseNet model for classification. It provides a web interface for users to upload images for analysis and download results.

Table of Contents

• Project Description
• Features
• Technologies Used
• Installation
• Usage
• API Endpoints
• Contributing
• License

Project Description

Chest X-rays are essential for diagnosing various lung diseases. This project uses deep learning models to assist in identifying diseases such as:

• Atelectasis
• Cardiomegaly
• Effusion
• Infiltration
• Mass
• Nodule
• Pneumonia
• Pneumothorax
• Consolidation
• Edema
• Emphysema
• Fibrosis
• Pleural Thickening
• Hernia

The project features a Flask web application where users can upload chest X-ray images and receive analysis results, including Grad-CAM visualizations.

Features

• Upload and analyze chest X-ray images.
• Object detection using YOLO model.
• Multi-class classification using DenseNet model.
• Grad-CAM visualization for interpretability.
• Downloadable results in ZIP format.

Technologies Used

• Python 3.10
• Flask
• Keras
• TensorFlow
• YOLO (Ultralytics)
• PIL (Pillow)
• HTML/CSS/JavaScript for the front-end

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/chest-xray-disease-multi-classification.git
   cd chest-xray-disease-multi-classification

Set up a virtual environment (optional but recommended):

bash

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

Install the required packages:

bash

pip install -r requirements.txt

Usage

Run the Flask application:

bash

python app.py

Open your web browser and go to http://127.0.0.1:5000.

Upload a chest X-ray image and click "Upload & Analyze" to see the results.

API Endpoints

POST /yolo_predict
    Upload an image to detect objects using the YOLO model.
    Returns the image with bounding boxes for detected objects.

POST /densenet_predict
    Upload an image for multi-class classification using the DenseNet model.
    Returns a ZIP file with Grad-CAM visualizations.

Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request for any improvements or features. License

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

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Instructions to Customize

• Project Description: Feel free to adjust the description to match your specific goals or objectives.
• Clone Link: Replace the https://github.com/yourusername/chest-xray-disease-multi-classification.git with the actual URL of your repository.
• License: If you have a specific license for your project, make sure to include it in the LICENSE file and update the reference accordingly.

You can create a README.md file in the root of your project directory and copy the content above into it. Let me know if you need any modifications or additional sections!