Image-Based Diagnostic Assistant

Overview

The Image-Based Diagnostic Assistant is a cutting-edge web application designed to assist in diagnosing diseases from medical images. Utilizing advanced deep learning models, this tool provides accurate diagnostic predictions for a variety of medical conditions. The application features a user-friendly interface where medical professionals or basic users can upload images, receive predictions, and access detailed information about the diagnostic process.

Features

• Image Upload: Users can upload medical images (e.g., MRI, X-ray) for analysis.
• Diagnostic Predictions: The application provides diagnostic predictions based on the uploaded images using state-of-the-art deep learning models.
• Informative Pages: The app includes informational pages such as About, Contact, FAQ, Privacy Policy, and Terms and Conditions.
• Production-Ready: The backend is built using Flask and Gunicorn, while the frontend is developed with React, ensuring scalability and reliability.
• Medical Libraries and AI: Integrates medical libraries and AI architectures like CNNs, U-Net, ViT, and more for enhanced diagnostic capabilities.

Tech Stack

• Frontend: React, JavaScript, HTML, CSS
• Backend: Flask, Gunicorn, Python
• Database: SQLite
• AI Models: TensorFlow, Keras, PyTorch
• Deployment: Docker, Heroku

Installation

To run the application locally, follow these steps:

Steps

1. Clone the repository:

   git clone https://github.com/your-repository.git
   cd your-repository

2. Setup Backend:

   cd backend
   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Setup Frontend:

   cd ../frontend
   yarn install

4. Build and Run Docker Containers:

   cd ..
   docker-compose up --build

Usage

• The frontend can be accessed at http://localhost:3000.
• The backend can be accessed at http://localhost:8000.

Configuration

Configuration files are located in the config/ directory. Ensure that the paths and settings in config.yaml match your setup.

config/config.yaml

model_path: 'models/path_to_model/model.h5'
upload_folder: 'uploads/'
allowed_extensions: ['jpg', 'jpeg', 'png']

API Endpoints

The backend provides the following API endpoints:

• GET /: Home page with welcome message.
• GET /upload: Page to upload images.
• POST /upload: Endpoint to handle image uploads and return predictions.
• GET /about: About page with information about the application.
• GET /contact: Contact page for user inquiries.
• GET /faq: FAQ page with frequently asked questions.
• GET /privacy: Privacy policy page.
• GET /terms: Terms and conditions page.

Example Usage

Upload an Image

1. Navigate to the /upload page.
2. Select an image of a skin lesion and upload it.
3. The application will process the image and provide a diagnostic prediction.

View Results

• After uploading, the /result page will display the prediction (benign or malignant) along with confidence scores.

Future Enhancements

We have several enhancements planned for the future:

• Model Improvements: Continuous improvement of model accuracy through data augmentation and transfer learning.
• Enhanced User Interface: Development of a more interactive and user-friendly interface.
• Additional Diagnostic Tools: Integration of more diagnostic tools and support for other types of medical images.
• Mobile Compatibility: Making the application fully compatible with mobile devices.
• Multi-Language Support: Adding support for multiple languages to cater to a global audience.

Results and Impact

The Image-Based Diagnostic Assistant has shown significant potential in assisting medical professionals with diagnostic tasks. The AI models have achieved high accuracy rates, providing reliable predictions that can aid in early detection and treatment of various medical conditions. This tool can potentially reduce the workload on healthcare providers and improve patient outcomes through timely and accurate diagnoses.

Contributing

I welcome contributions from the community. To contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes with descriptive messages.
4. Push your changes to your fork.
5. Submit a pull request to the main repository.

Please ensure your code adheres to the coding standards and includes appropriate tests.

License

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

Contact

For questions, feedback, or collaboration opportunities, please reach out to me at robinsonjason761@gmail.com.

Acknowledgements

I would like to thank the following resources and communities for their support and contributions:

• TensorFlow
• Keras
• Hugging Face
• OpenCV
• The open-source community for their valuable libraries and tools.