Retinal Image Analysis for Diabetic Retinopathy Detection

Abstract

Diabetic Retinopathy (DR) is a severe ocular complication resulting from diabetes, characterized by damage to the retinal blood vessels. This condition can occur in individuals with either type 1 or type 2 diabetes and is exacerbated by prolonged hyperglycemia. As the retinal vessels deteriorate, they may become blocked or leak, leading to compromised blood supply, loss of vision, and, in some cases, irreversible damage due to the formation of scar tissue. The conventional approach to examining fundus images for DR diagnosis is often cumbersome and time-consuming, requiring significant manual analysis to detect subtle differences in retinal morphology. In this study, we propose a Customized Convolutional Neural Network (CCNN) as an advanced deep learning technique for the automated detection of Diabetic Retinopathy. Our methodology follows a structured workflow encompassing essential phases such as input data retrieval, data preprocessing, segmentation, feature extraction, model creation, training, testing, and interpretation of results. By employing this systematic approach, we aim to enhance the efficiency and accuracy of DR detection, ultimately contributing to improved patient outcomes.The performance evaluation is conducted using the MESSIDOR dataset, which includes 560 images for training and 163 images for testing. Our proposed model achieved a notable test accuracy of 97.24%, indicating a significant improvement over existing algorithms in terms of detection accuracy. The experimental results underline the potential of deep learning models in revolutionizing the traditional diagnostic process, allowing for faster and more reliable assessments of Diabetic Retinopathy.Through this research, we not only highlight the importance of leveraging advanced machine learning techniques in medical diagnostics but also provide insights into the potential future applications of such technologies in broader healthcare settings. By reducing the reliance on manual examination methods, our CCNN approach presents a viable solution to the pressing challenges posed by Diabetic Retinopathy diagnosis and management.