Review on Diabetic Retinopathy Detection Using Molecular Segmentation In Convolution Neural Network

Abstract

Abstract: People who suffer from diabetes are at risk of developing blindness since their pancreas doesn't produce enough insulin. Diabetic retinopathy is a condition in which a patient's vision gradually worsens as the disease advances. To better understand how diabetes affects the eye, retinal images captured with a fundal camera can be helpful. This study aims to detect blood vessels, identify hemorrhages, and classify different phases of diabetic retinopathy into normal, mild, and non-proliferative diabetic retinopathy (NPDR). When it comes to classifying the various stages of diabetes-related retinopathy, the retinal picture is used as a starting point. Using the contrast between the blood vessels and the surrounding background, the retinal vascular is segmented.. Density analysis and bounding box approaches were used to identify potential hemorrhage sites. According to blood vessel and hemorrhage area and perimeter, Random Forests technique was used to finally classify various phases of eye disease. Keywords: Retina, blood vessel, hemorrhages, classification, diabetic retinopathy.