This repository contains the code and resources for a deep learning project focused on ocular disease recognition. The goal of this project is to develop and train convolutional neural networks (CNNs) to accurately classify ocular images into different disease categories. The models are trained to identify a variety of eye diseases, including diabetic retinopathy, cataracts, and glaucoma.
The dataset for this project can be obtained from the following link:
Ocular Disease Recognition (ODIR) Dataset
About this Dataset
Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database consisting of data from 5,000 patients. Each patient's data includes age information, color fundus photographs from the left and right eyes, and diagnostic keywords provided by doctors.
This dataset is intended to represent a "real-life" collection of patient information gathered by Shanggong Medical Technology Co., Ltd. from various hospitals and medical centers in China. In these institutions, fundus images are captured using various cameras available in the market, such as Canon, Zeiss, and Kowa, resulting in varying image resolutions.
To get started with this project, follow these steps:
- Clone this repository to your local machine.
git clone https://github.com/aha009/Ocular-Disease-Recognition.git
- Install the required dependencies listed in
requirements.txt.
pip install -r requirements.txt
We welcome contributions from the community. If you have any ideas, bug fixes, or improvements, please feel free to open an issue or submit a pull request. For major changes, please discuss them in advance.
This project is licensed under the MIT License - see the LICENSE file for details.