Chest X-ray Abnormalities Detection

This project focuses on detecting abnormalities in chest X-rays using the YOLOv8 model and is deployed as a web application via Flask.

🎯 Objective

To assist healthcare professionals in quickly and conveniently identifying abnormalities in patients' chest X-rays, enhancing the capability for accurate medical diagnosis.

📦 Data Source

• The project utilizes data from VinBigData.

🚀 Usage

1. Start the Application:

   python predict_api.py
   

2. Access the Application:

   Open a browser and navigate to http://127.0.0.1:5000.

3. Upload and Analyze:

   • Drag and drop or select the chest X-ray image you wish to analyze.
   • Click 'Upload file' to initiate the analysis and view the results.

⚙️ Installation

1. Requirements:

   • Python 3.x
   • Libraries: flask, yolov8, ...

2. Setup:

   git clone https://github.com/TrDung22/Chest-X-ray-Abnomolities-Detection.git
   pip install -r requirements.txt
   

🌐 Interface

• Home Page:

  Here, users can upload X-ray images and receive analysis results.

• Image Analysis:

  The uploaded image is analyzed by the YOLOv8 model, with results promptly displayed on the web page.

💡 Notes

• Ensure the uploaded image is in the right format and not oversized.
• The application is optimized primarily for chest X-rays.