Design and Implementation of CNN-based Diabetic Retinopathy Detection

Abstract

Diabetic Retinopathy (DR), a complication of diabetes, is one of the primary causes of blindness across the globe. Detecting subtle abnormalities in retinal images, such as microaneurysms, hemorrhages, exudates, and changes in the macula, is crucial for diagnosing DR. However, manual evaluation of these images is a tedious and time-intensive process. Computer-aided systems can enhance the accuracy and efficiency of this task, enabling timely intervention to prevent or slow down vision loss. Artificial intelligence (AI), particularly in recent years, has emerged as a powerful tool for automating the detection and classification of DR in retinal fundus images.
This study explores the application of advanced deep-learning models to identify diabetic retinopathy in retinal images. Specifically, we employed Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in computer vision tasks, including medical image analysis. A publicly accessible dataset from Kaggle was utilized to train and validate the model, ensuring the reproducibility and scalability of the proposed approach.
We designed a web app to give medical practitioners support and a trustworthy second opinion on disorders that can be discovered through medical imaging. Using artificial intelligence, the platform analyzes these medical photos and classifies them into several categories. This program is intended to provide you with accurate, dependable, and quick results.