🏥 MedVision AI

Portable AI-Powered Multi-Disease Screening System

A portable, low-cost embedded healthcare device enabling real-time, non-invasive multi-disease screening using AI-driven vision and biosensing.

Features

• Eye Disease Screening — Detects Cataract, Diabetic Retinopathy, Glaucoma
• Skin Lesion Analysis — Benign vs Malignant classification
• Hemoglobin Estimation — Non-invasive anemia risk flagging
• Diabetes Risk Assessment — ML-based glucose pattern analysis
• RAG-Based Reports — Doctor-ready structured medical reports
• PDF Download — Print-ready diagnostic reports
• Webcam / ESP32-CAM — Camera integration for screening
• Patient Management — Store up to 5 patient records

Tech Stack

Layer	Technology
Frontend	React + Vite
Backend	Python Flask
AI/ML	Scikit-learn, MobileNetV2, EfficientNet, ResNet
Reports	jsPDF
Hardware	ESP32-CAM
Deployment	Render

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Quick Start

Prerequisites

Before you start, make sure you have the following installed:

• Node.js 18+
• Python 3.10+
• pip package manager

1. Clone & Setup

Clone the repository to your local machine:

git clone https://github.com/ATHARVA316-DEV/MedVision_Al.git
cd MedVision_Al

2. Backend Setup

Set up the Python Flask API and train initial demo ML models.

cd backend
pip install -r requirements.txt
python training/create_demo_models.py  # Train AI models
python app.py                           # Start Flask server (port 5000)

3. Frontend Setup

In a new terminal window, navigate to the frontend directory:

cd frontend
npm install
npm run dev  # Start dev server

4. View Dashboard

Navigate to http://localhost:5173 in your browser.

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📷 ESP32-CAM Setup

MedVision AI supports live camera feeds for non-invasive screening via the ESP32-CAM module.

1. Open esp32/medvision_cam.ino in the Arduino IDE.
2. Update the ssid and password with your network credentials.
3. Upload the firmware to your ESP32-CAM.
4. Note the allocated IP address from the Serial Monitor (115200 baud).
5. Enter the stream URL in the dashboard: http://<ip>:81/stream.

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☁️ Deployment (Render)

Deploying MedVision AI to the cloud is simplified using Render.

1. Push your code to GitHub.
2. Connect your repository to Render.
3. The included render.yaml configuration file will automatically define services.
4. Click Deploy!

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🔌 Hardware Setup

📷 System Overview

📂 Project Structure

MedVision_Al/
├── frontend/          # React + Vite application
│   ├── src/
│   │   ├── pages/     # LandingPage, Dashboard, ReportPage
│   │   └── ...
│   └── package.json
├── backend/           # Flask API + ML models
│   ├── app.py         # Main Flask server entry point
│   ├── models/        # Trained .pkl models
│   ├── training/      # Model training scripts
│   └── data/          # Patient data storage (JSON)
├── esp32/             # Arduino firmware for ESP32-CAM
├── ml_models/         # Additional ML model scripts/utilities
└── render.yaml        # Infrastructure-as-code for Render

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