Detection of Diabetic Retinopathy Image Analysis using Convolution Graph Neural Network

Abstract

Diabetic retinopathy is caused by high blood sugar levels, which damage and obstruct the small blood vessels in the retina. The eye creates new blood vessels to make up for the diminished blood flow. These don't work properly and result in blood loss, which impairs eyesight. 1) A vascular hemorrhage might occur if diabetes is not treated and blood sugar levels are not under control. 2) Retinal separation 3) glaucoma-induced blindness. Expertise and technology are generally scarce in areas where the diagnosis of diabetic retinopathy is most important. The majority of research in the field of diabetic retinopathy has been concentrated on disease detection or manually extracting features, but this study hopes to apply deep learning to automatically diagnose the condition in its many stages. This study focuses on the automated identification and categorization of diabetic retinopathy symptoms using a pre-trained convolutional graph neural network (CGNN). A collection of retinal images are used from the eyepacs diabetic retinopathy database., which was categorized into five groups based on the severity of retinal damage. Images from this database were preprocessed and utilized as training and testing data. In addition to being more effective and accurate than manual tests, this method of DR detection also conserves time and money. The transfer learning paradigm used by Alexnet incorporates CGNN technology. In this study, accuracy above 90% is predicted and also plotted the confusion matrix and ROC curve using Matlab.