https://github.com/anhtuan284/chest-xray-multi-disease
Multi-disease segmentation chest X-rays by YOLO and DenseNet121, CoAtNet models
https://github.com/anhtuan284/chest-xray-multi-disease
chest-xray-images computer-vision deep-learning densenet121 flask-api flutter-apps yolo
Last synced: 3 months ago
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Multi-disease segmentation chest X-rays by YOLO and DenseNet121, CoAtNet models
- Host: GitHub
- URL: https://github.com/anhtuan284/chest-xray-multi-disease
- Owner: anhtuan284
- Created: 2024-09-02T08:01:07.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2025-03-29T04:29:02.000Z (3 months ago)
- Last Synced: 2025-03-29T05:19:51.151Z (3 months ago)
- Topics: chest-xray-images, computer-vision, deep-learning, densenet121, flask-api, flutter-apps, yolo
- Language: Jupyter Notebook
- Homepage:
- Size: 85 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](#project-description)
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
- [Usage](#usage)
- [API Endpoints](#api-endpoints)
- [Contributing](#contributing)
- [License](#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:
```bash
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.
sql
### 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!