DiagnoAssist

Overview

The DiagnoAssist is a web-based application designed to assist healthcare professionals in diagnosing diseases by analyzing medical images, patient data, and symptoms. The application leverages AI/ML models for medical image analysis and symptom interpretation, providing accurate and efficient diagnostic suggestions.

Features

1. Medical Image Analysis:

   • Upload and analyze medical images (e.g., X-rays, MRIs) using AI models.

   • Detect abnormalities such as tumors, fractures, or pneumonia.

2. Symptom Interpretation:

   • Input patient symptoms and receive potential diagnoses.

   • Uses NLP to interpret and correlate symptoms with known disease patterns.

3. Predictive Analytics:

   • Predict diseases based on patient data (e.g., age, gender, medical history).

4. User-Friendly Interface:

   • Intuitive frontend for healthcare professionals to input data and view results.

Technologies Used

• Frontend: React.js (Vite)

• Backend: Flask (Python)

• AI/ML: Medical image analysis (CNNs), symptom interpretation (NLP), and predictive analytics (ML algorithms) to provide accurate, efficient, and data-driven diagnostic suggestions for healthcare professionals. It leverages frameworks like TensorFlow, PyTorch, and spaCy for model training and deployment.

• Database: SQLite (for local development)

• Deployment: AWS, Google Cloud, or Heroku

Prerequisites

Before running the project, ensure you have the following installed:

• Node.js (for frontend)

• Python 3.8+ (for backend)

• pip (Python package manager)

• Git (for version control)

Setup Instructions

1. Clone the Repository

git clone https://github.com/vaishnavidalal11/Google-Girl-Hackathon.git
cd Google-Girl-Hackathon

2. Set Up the Backend

1. Navigate to the backend folder:

   cd backend

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\\Scripts\\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

5. Run the Flask server:

   python app.py

   The backend will start at http://127.0.0.1:5000.

3. Set Up the Frontend

1. Navigate to the frontend folder:

   cd frontend

2. Install dependencies:

   npm install

3. Set Up Tailwind CSS:
   • Install Tailwind and its dependencies:

     npm install -D tailwindcss postcss autoprefixer

   • Initialize Tailwind:

     npx tailwindcss init -p

   • Configure tailwind.config.js:

     /** @type {import('tailwindcss').Config} */
     export default {
         content: [
             "./index.html",
             "./src/**/*.{js,ts,jsx,tsx}",
         ],
         theme: {
             extend: {},
         },
         plugins: [],
     }

   • Add Tailwind to your CSS file (src/index.css):

     @tailwind base;
     @tailwind components;
     @tailwind utilities;

4. Start the development server:

   npm run dev

   The frontend will start at http://localhost:5173.

Usage

1. Upload Medical Images:

   • Use the "Upload Image" feature to analyze medical images.

   • View diagnostic results with confidence scores.

2. Input Patient Data:

   • Enter patient details (e.g., age, gender, symptoms) in the form.

   • Receive potential diagnoses based on the input.

3. View Results:

   • Diagnostic suggestions are displayed with confidence scores.

   • Recommendations for further tests or treatments are provided.

Dataset

The project uses the following datasets for training and testing AI models:

• CheXpert: CheXpert Dataset

• NIH Chest X-ray Dataset: NIH Dataset

• MedMNIST: MedMNIST

Deployment

Backend

1. Deploy the Flask backend on Heroku:

   • Install the Heroku CLI.

   • Create a Procfile:

     web: python app.py

   • Push your code to Heroku:

     heroku create
     git push heroku main

Frontend

1. Deploy the React frontend on Netlify or Vercel:

   • Build the project:

     npm run build

   • Upload the dist folder to your chosen platform.

Expected Outcomes

• Improved Diagnostic Accuracy: AI models provide precise disease detection.

• Faster Diagnoses: Reduces the time required for analysis.

• Scalability: Can be deployed in hospitals and clinics worldwide.

• Real-World Impact: Enhances healthcare delivery, especially in underserved areas.

Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Commit your changes:

   git commit -m "Add your feature"

4. Push to the branch:

   git push origin feature/your-feature-name

5. Open a pull request.