DiabEye : Detecting DR using Deep Learning

Welcome to the Diabetic Retinopathy Detection App repository. This project is designed to detect diabetic retinopathy using image classification models. The app is hosted online and can be accessed here.

Table of Contents

• About the Project
• Folder Structure
• Getting Started
• System Requirements
• License
• Contributing Members

About the Project

The Diabetic Retinopathy Detection App leverages advanced image classification techniques to identify signs of diabetic retinopathy in retinal images. The application aims to provide an accessible and efficient tool for early detection, aiding in timely medical intervention.

Folder Structure

The repository is organized into the following folders:

• app: Contains the code for the web application.
• code: Notebooks for model training and image classification.
• images/val: Images used for validation purposes.
• pretrained_models: Folder containing models trained using the notebooks in the code folder.
• reference: Reference notebooks and testing scripts.

app

The app folder contains the code for the Streamlit web application. This includes scripts to run the app, manage user input, and display predictions.

code

The code folder contains Jupyter notebooks used for model training and image classification. These notebooks include data preprocessing, model architecture, training routines, and evaluation metrics.

images/val

The images/val folder holds the images used for validation. These images are used to test the performance and accuracy of the trained models.

pretrained_models

The pretrained_models folder contains the models that were trained using the notebooks in the code folder. These models can be loaded and used by the app for making predictions.

reference

The reference folder includes additional notebooks and testing scripts that provide further context and validation for the project. These are useful for understanding the development and testing process.

Getting Started

To get started with the project, follow these steps:

Clone the repository:

git clone https://github.com/eebadreza/diabetic-retinopathy-detection.git

System Requirements

The Diabetic Retinopathy Detection App was developed and tested under the following system specifications:

• Hardware:

  • Apple Silicon M1
  • 8 GB RAM

• Software:

  • Python 3.12.3
  • PyCharm (for development)
  • Other dependencies listed in the requirements.txt file located in the app folder.

License

This project is licensed under the MIT License.

MIT License Copyright (c) 2024 Eebad Reza Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation

Contributing Members

• Eebad Reza
• Anuragh Singh
• Tanmita Roy
• Namrata Singh

We have completed this project and no further contributions are required at this time. Thank you for your interest.