A machine learning project that classifies retinal images to detect Diabetic Retinopathy (DR) using deep learning techniques. This repository provides the code, data references, and steps to build a fully functional model.
Diabetic Retinopathy is a diabetes complication that affects the eyes and can lead to blindness. Early diagnosis through automated image analysis helps in preventing severe damage.
This project uses a convolutional neural network (CNN) with transfer learning to identify the severity levels of DR from retinal fundus images.
The development of an automated diabetic retinopathy screening system can help detect a leading cause of blindness in individuals with diabetes. By training a neural network on retinal photographs from both affected and healthy individuals, this research aims to determine the presence or absence of retinopathy in patients.
Prerequisites
- Python 3.7+
- Libraries: TensorFlow, Keras, OpenCV, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn
Install dependencies:
pip install -r requirements.txtUse publicly available datasets:
Project Under Construction