Diabetic Retinopathy Detection Using Convolutional Neural Network (CNN)

Overview

This project aims to develop a machine learning model for detecting diabetic retinopathy from retinal images. Diabetic retinopathy is a medical condition in which damage occurs to the retina due to diabetes. Early detection through retinal image analysis can prevent severe vision loss. Our model leverages deep learning techniques to identify signs of diabetic retinopathy in retinal images.

Table of Contents

• Overview
• Dataset
• Model Architecture
• Installation
• Usage
• License

Dataset

The dataset used for this project includes retinal images labeled for the presence and severity of diabetic retinopathy. The dataset can be obtained from Kaggle's Diabetic Retinopathy Detection competition.

Model Architecture

The model is based on a Convolutional Neural Network (CNN) architecture. Key features include:

• Preprocessing: Image resizing, normalization, and augmentation.
• CNN Layers: Multiple convolutional layers with ReLU activation and max-pooling.
• Fully Connected Layers: Dense layers leading to a softmax output for classification.

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/diabetic-retinopathy-detection.git
   cd diabetic-retinopathy-detection

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

Usage

To train and evaluate the model, use the following commands:

1. Train the model:

   python train.py --dataset_path path/to/dataset

2. Predict on new images:

   python predict.py --model_path path/to/saved_model --image_path path/to/image

License

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