Diabetic Retinopathy Detection..

Introduction

This project is focused on employing machine learning techniques to detect diabetic retinopathy using fundus images of the retina. Diabetic retinopathy is a severe complication of diabetes affecting the eyes, potentially leading to blindness if not addressed.

Technologies Used

The technologies utilized in this project include:

• Python: Backend programming language.
• Flask: Web framework for the application.
• TensorFlow/Keras: Deep learning library used for model development and predictions.
• JSON: Data format for storing remedies associated with different stages of retinopathy.
• HTML/CSS/JS: Frontend development for user interaction.

Overview

The project consists of a Flask-based web application that serves as an interface for diabetic retinopathy detection. It involves the following functionalities:

1. Model Loading: Load a pre-trained machine learning model capable of detecting retinopathy from fundus images.

2. Image Processing: Process uploaded fundus images for prediction.

3. Prediction and Remedies: Provide predictions on the severity of diabetic retinopathy and suggest remedies based on the prediction outcome.

Instructions to Run

Prerequisites

Ensure the following requirements are met:

• Python: Ensure Python 3.x is installed on your system.

Setup

1. Clone the Repository:

   git clone https://github.com/your-username/your-project.git
   cd diabetic_retinopathy

2. Install Dependencies:

   pip install -r requirements.txt

3. Prepare Model and Files:

   • Place your trained model file (model.h5) in a folder named model within your project directory.
   • Ensure the remedies.json file contains remedies associated with different stages of retinopathy.

Running the Application

4. Run the Flask Application:

   python app.py

5. Access the Application:

   • Open a web browser and visit http://localhost:5000 (or the address specified in your Flask code if modified) to access the Diabetic Retinopathy Detection application.
   • Use the provided interface to upload fundus images and view the predictions and suggested remedies.

6. Troubleshooting:

   • If any issues occur, ensure proper file paths in your Flask code for loading the model ('model/model.h5') and the remedies JSON file ('remedies.json').
   • Check the console or terminal for error messages that might help identify problems.