Diabetic Retinopathy Detection using Machine Learning

Abstract

Diabetic retinopathy is a leading cause of vision loss worldwide, and early detection through retinal image analysis is critical for preventing severe complications. However, traditional screening methods are time-consuming and rely heavily on expert interpretation, often leading to delayed diagnosis and limited accessibility [1], [10]. This paper presents a deep learning-based diabetic retinopathy detection system using Convolutional Neural Networks (CNNs) to improve classification accuracy of retinal fundus images. The proposed system preprocesses retinal images, extracts relevant features, and classifies them into multiple severity stages by leveraging CNNs’ ability to learn complex spatial patterns and visual features automatically [3], [4]. Furthermore, the integration of regularization techniques enhances model generalization and reduces overfitting, enabling reliable performance even on moderate-sized datasets [5]. The developed system demonstrates the potential of deep learning in supporting automated, fast, and accessible diagnosis of diabetic retinopathy, contributing to improved healthcare outcomes.