Ensemble Architecture for Prediction of Grading of Diabetic Retinopathy

Abstract

Diabetic Retinopathy (DR) is a diabetic issue that influences the eyes. It harms the veins of the light-delicate tissue behind the eye (retina). DR is an issue of diabetes and the main source of visual deficiency. Since there are trained doctors to diagnose the disease, but to make their work easier, ensemble architectures were proposed for the prediction of grading of DR using modern technology known as deep learning. Deep neural network results in a better diagnostic system in comparison to machine learning networks. ResNET 101 and Ensemble CNN architectures were proposed using Adam Optimization for the detection of DR. From the results, it has been observed that for any constant size Network architecture, the loss cannot be further reduced after a certain number known as Bayes error. With the help of a learning curve, it can easily understand that the proposed architecture costs are decreasing after every iteration. The Ensemble and ResNET 101 model attain 87.67% and 81.28% accuracy, respectively, and 0.5046 and 0.5328 cross-entropy loss, respectively. The proposed Ensemble model results in an 18.4% improvement over the Inception V3 model and 7.8% over the proposed ResNet 101 model.