Detection And Classification Of Diabetic Retinopathy Using Deep Learning: A Review

Abstract

Diabetic retinopathy (DR) is a common complication of diabetes and a leading cause of vision loss worldwide. Early detection and classification of DR lesions are crucial for timely intervention and prevention of irreversible vision impairment. In recent years, deep learning techniques have shown promising results in automating the detection and classification of DR from retinal images, offering potential solutions to address the increasing burden on healthcare systems. This paper presents a comprehensive review of deep learning approaches for DR detection and classification, focusing on the advancements made in convolutional neural networks (CNNs) and their applications in analyzing retinal images. By synthesizing existing literature and empirical studies, this review highlights key methodologies, datasets, and performance metrics utilized in DR detection and classification tasks. Furthermore, it discusses challenges such as dataset imbalance, model interpretability, and generalization to diverse populations. Through this review, stakeholders in healthcare and computer vision gain insights into the current state-of-the-art techniques and future directions for improving DR diagnosis and management.