/AI-Powered-CXR-Diagnostic-System

Welcome to the AI-Powered CXR Diagnostic System! This project utilizes advanced AI and machine learning techniques to streamline the radiologist workflow by automating the analysis of chest X-ray (CXR) images.

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AI-Powered CXR Diagnostic System

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Overview

Welcome to the AI-Powered CXR Diagnostic System! This project utilizes advanced AI and machine learning techniques to streamline the radiologist workflow by automating the analysis of chest X-ray (CXR) images. Built with PyTorch and Flask, this system uses a DenseNet121 pretrained model from torchxrayvision to detect 14 different pathologies with high accuracy, making the diagnostic process faster and more efficient.

Features

  • Accurate Detection: Leverages the DenseNet121 model from torchxrayvision for precise identification of 14 different pathologies in CXR images.
  • User-Friendly Interface: Simple and intuitive web interface for uploading and analyzing images.
  • Real-Time Results: Provides quick diagnostic results to assist radiologists in making informed decisions.

Pathologies Detected

This system can detect the following 14 pathologies from the NIH ChestX-ray14 dataset:

  1. Atelectasis
  2. Cardiomegaly
  3. Effusion
  4. Infiltration
  5. Mass
  6. Nodule
  7. Pneumonia
  8. Pneumothorax
  9. Consolidation
  10. Edema
  11. Emphysema
  12. Fibrosis
  13. Pleural Thickening
  14. Hernia

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Technologies Used

Python PyTorch Flask GitHub

Installation

  1. Clone the Repository:

    git clone https://github.com/HuzaifaKhaan/AI-Powered-CXR-Diagnostic-System.git
cd AI-Powered-CXR-Diagnostic-System

  2. Create a Virtual Environment and Activate It:

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

  3. Install the Dependencies:

    pip install -r requirements.txt

Usage

  1. Run the Flask Application:

    python app.py

  2. Open Your Browser and Navigate to:

    http://127.0.0.1:5000

  3. Upload a CXR Image:

    • Click on the "Upload" button and select a CXR image from your local machine.

  4. Get Diagnostic Results:

    • The system will analyze the image using the DenseNet121 model and display the diagnostic results on the screen.

Project Structure

.
├── app.py                # Main application file
├── requirements.txt      # Project dependencies
├── static/               # Static files (CSS, JS, images)
├── templates/            # HTML templates
├── testing_images/       # Directory for test images
└── .vscode/              # VSCode configuration files

Feel free to reach out if you have any questions or suggestions!

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