Be Cured - AI for Diabetic Retinopathy & Kidney Disease

Introduction

Be Cured is an AI-powered healthcare solution for the early diagnosis of diabetic retinopathy and kidney disease. The system leverages deep learning models for medical imaging analysis and clinical data processing, enabling early detection and risk assessment.

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Installation & Setup

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Flask
• TensorFlow
• Scikit-Learn
• NumPy, Pandas, Matplotlib, Seaborn

Installation Steps

1. Clone the repository:

   git clone https://github.com/gurarpitzz/BeCured.git
   cd BeCured

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask application:

   python app.py

4. Access the web app:
   Open your browser and go to http://127.0.0.1:5000/

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Usage Guide

1. Upload Medical Data

• For Diabetic Retinopathy: Upload a retinal scan image.
• For Kidney Disease: Upload clinical data (CSV/Excel).

2. AI Model Analysis

• The system uses CNNs for image-based disease detection.
• Clinical data is processed for risk assessment.

3. View Diagnostic Results

• The web interface displays disease presence, severity levels, and recommendations.
• Users can download a health report in PDF format.

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Project Structure

📂 BeCured
│-- 📂 static                 # Static files (CSS, JS, Images)
│   │-- style.css             # Styling for the UI
│   │-- script.js             # (Optional) JavaScript for interactivity
│   │-- images/               # (Optional) Image assets
│
│-- 📂 templates              # HTML templates for Flask
│   │-- index.html            # Main UI page (File upload & results display)
│   │-- result.html           # Displays diagnosis results
│
│-- app.py                    # Main Flask application, handles requests and AI processing
│-- Dibetic_Retinopology.ipynb # Jupyter Notebook for Retinopathy analysis
│-- Kidney_Disease_Analysis.ipynb # Jupyter Notebook for Kidney Disease
│-- model.h5                  # Trained model for Retinopathy detection
│-- kidney_disease_model.pkl   # Trained model for Kidney Disease prediction
│-- health_report.pdf         # Sample generated health report
│-- README.md                 # Documentation
│-- requirements.txt          # Required Python packages

File Descriptions

File/Folder	Description
app.py	Main Flask application. Handles data input, model processing, and result display.
templates/index.html	Main frontend page for uploading files and viewing results.
templates/result.html	Displays the AI-generated diagnosis and recommendations.
static/style.css	CSS styles for the frontend.
static/script.js	(Optional) JavaScript for frontend interactivity.
Dibetic_Retinopology.ipynb	Jupyter Notebook for analyzing diabetic retinopathy data.
Kidney_Disease_Analysis.ipynb	Jupyter Notebook for kidney disease analysis.
model.h5	Pretrained deep learning model for retinal disease detection.
kidney_disease_model.pkl	Machine learning model for kidney disease prediction.
health_report.pdf	Sample output report for AI-generated results.
requirements.txt	Contains all Python dependencies required for the project.
README.md	This documentation file.

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Tech Stack

• Machine Learning & AI: TensorFlow, Scikit-Learn
• Deep Learning: CNNs for image classification
• Web Framework: Flask (Python)
• Frontend: HTML, CSS, JavaScript
• Data Visualization: Matplotlib, Seaborn

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License

This project is open-source under the MIT License.

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