Medical Scan Analysis APP

A FastAPI-based application for medical image analysis using deep learning models. The application supports detection of multiple medical conditions through various scanning modalities.

Supported Conditions

• Brain Tumor Detection
• Tuberculosis Detection
• Lung Cancer Detection
• COVID-19 Detection
• Pneumonia Detection
• Kidney Disease Detection
• Knee Osteoporosis Detection
• Diabetic Retinopathy Detection

Prerequisites

• Python 3.9+
• Kaggle account and API credentials
• Git

Installation

1. Clone the repository

git clone https://github.com/yourusername/Medical-Scan-App.git
cd Medical-Scan-App

2. Create a virtual environment

python -m venv venv
# For Windows
.\venv\Scripts\activate
# For Linux/Mac
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

Running the Application

1. Start the FastAPI server:

uvicorn main:app --reload

2. Access the API documentation:

• OpenAPI documentation: http://localhost:8000/docs

API Endpoints

Each medical condition has its own endpoint for predictions:

• /Brain-Tumor/predict - Brain tumor detection
• /Tuberculosis/predict - TB detection
• /Lung-Cancer/predict - Lung cancer detection
• /Covid/predict - COVID-19 detection
• /Pneumonia/predict - Pneumonia detection
• /Kidnee/kidney/predict - Kidney disease detection
• /Kidnee/knee/predict - Knee disease detection
• /Diabetic-Retinopathy/predict - Diabetic retinopathy detection

Models

The application uses pre-trained models hosted on Kaggle:

• Brain Tumor: ResNet model
• Tuberculosis: ResNet model
• Lung Cancer: ResNet model
• COVID-19: Custom CNN
• Pneumonia: Inception model
• Kidney Disease: ResNet50
• Diabetic Retinopathy: ResNet50

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Models trained using Kaggle datasets
• FastAPI framework for API development
• TensorFlow for deep learning models

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🩺 Scanalyze-Medical-Chatbot

A multilingual conversational AI API for medical question answering.
This API leverages state-of-the-art language models (Apollo-7B, AraBERT) with retrieval-augmented generation (RAG), built on FastAPI for blazing-fast performance.

It is designed to:

• Understand medical questions in Arabic and English
• Retrieve relevant context from a Qdrant vector database
• Generate accurate, context-aware responses using Hugging Face Transformers
• Run in cloud environments with Azure ML integration

Features

• Multilingual: Supports both Arabic and English medical queries.
• Large Language Models: Uses Apollo-7B for response generation.
• Contextual QA: Retrieves relevant documents from Qdrant to enhance answers.
• Secure Cloud Deployment: Integrated with Azure Machine Learning for model management.
• FastAPI: Lightweight, high-performance REST API.
• Response Cleanup: Removes repetitive or filler content for clean outputs.

Project Structure

• ├── app.py # FastAPI application with endpoints
• ├── model/ # Downloaded model from Azure ML
• ├── requirements.txt # Python dependencies
• ├── Dockerfile # (Optional) Containerization
• └── README.md # Documentation

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Tech Stack

Setup Instructions

Clone the repository

git clone [https://github.com/<your-username>/Scanalyze-Medical-Chatbot.git](https://github.com/khalednabawey/Scanalyze-Medical-Chatbot)
cd Scanalyze-Medical-Chatbot

Install dependencies

• Create a virtual environment and install requirements:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

License

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