A Study on Diabetic Retinopathy Classification using Deep Learning Technique

Abstract

Diabetic Retinopathy (DR) is a prevalent complication of diabetes mellitus, leading to retinal lesions that can cause vision impairment and even blindness if left undetected. The prevalence of diabetes worldwide has witnessed a significant increase in recent years, affecting individuals across all age groups. Early detection and treatment of DR are crucial to mitigate the risk of vision loss. However, the manual diagnosis of DR using retina fundus images is time-consuming, labor-intensive, costly, and prone to misdiagnosis. As a result, computer-aided diagnosis systems have emerged as promising alternatives. Among these, deep learning techniques, particularly convolutional neural networks (CNNs), have shown remarkable performance in various domains, including medical image analysis and classification. In this article, we present a comprehensive review and analysis of the state-of-the-art methods for the detection and classification of DR color fundus images using deep learning techniques. We highlight the effectiveness of CNNs in addressing the challenges associated with DR diagnosis. By leveraging large datasets, these deep learning models learn intricate patterns and features directly from the raw image data, enabling accurate classification of DR severity levels.