Diabetic Retinopathy Detection and Classification from Fundus Images Using Deep Learning

Abstract

Diabetic Retinopathy (DR) is one of the severe microvascular conditions that affect the retina of the eye. It can result in permanent partial or total blindness if not discovered and treated early. Across the world, diabetes impacts in excess of 50% of people below 70 years of age. Many diabetic people, however, fail to notice the disease and suffer visual impairments as a result of the time it takes to see an ophthalmologist who screens and analyses the patient’s retina. This research study focuses on the automated detection and categorization of DR based on distinct severity levels using fundus images. The method starts with a pre-processing step to get rid of any extraneous noise from the edges and aids in the focus on the area of interest. The DR fundus images were then categorized into various severity levels using DL models like VGG19, ResNet50, and Inception-V3 Deep Learning (DL) models. The logic for the presented DL model has been tested on the EyePACS (kaggle) DR dataset. In terms of classification accuracy, the suggested DL model with ResNet50 configuration surpassed other models such as VGG19 and Inception-V3 as well as other current models, according to the findings of the research.