A Diabetic Retinopathy Detection Using Customized Convolutional Neural Network

Abstract

The disease, Diabetic Retinopathy (DR) causes due to damage to retinal blood vessels in diabetic patients. DR occurs if you have type 1 or 2 diabetes along with high blood sugar. When the retinal blood vessels are damaged, they can become clogged, some of which can block the blood supply to the retina leading to blood loss, these new blood vessels may leak, and the creation of scar tissue can lead to loss of vision. It takes a lot of time and effort to examine and analyse fundus images the old-fashioned way to find differences in how the eyes are shaped. In this modern era, technology has evolved so fleet which has the solution to every problem. In this paper, we have proposed a Customized Convolutional Neural Network (CCNN) deep learning technique for Diabetic Retinopathy Detection. We have clung to traditional strategies mainly containing input Data retrieval, pre-processing of data, segmentation, trait measurement, feature extraction, model creation, model training, model testing, consequence, and interpretation of the model. Performance evaluation is done on standard MESSIDOR Dataset in which 560 images for training phase whereas 163 images for testing phase. The experiment results achieved the highest test accuracy of 97.24% which is effectively higher than that of existing algorithms.