Predicting Severity of Diabetic Retinopathy using Deep Learning Models

Abstract

This paper presents deep learning models for the classification of Diabetic Retinopathy (DR) grades. The goal of this research is to find and create a deep learning model that will help us identify the image with high accuracy into one of the five phases of the DR as no DR, mild, moderate, severe, and proliferative DR. The whole work is developed using four steps. The first, using Ben Graham's pre-possessing form, the fundus images were pre-processed. Secondly, in order to train the models, the preprocessed images are contributed to the deep learning algorithm. The third, deep learning models such as Deep CNN, Dense Net, and Group 19 Visual Geometry (VGG19) are developed to predict the severity of the DR. The APTOS Blindness Detection dataset is used to train the proposed deep learning models. Since the data set is imbalanced in nature, the issue of training bias contributes to it. Therefore, at the time of training the models, class weight technique is used to eliminate the training bias problem. In the case of DR grading structures, the proposed deep learning models work well. The Dense Net has been found to work better than the other two models.