An Analytical Perspective for Diabetic Retinopathy Using Convolutional Neural Network

Although the risk factors for diabetic retinopathy (DR) have been thoroughly investigated in previous studies, it is still unclear which risk factors are more closely associated with DR than others. The possibility that we will be able to differentiate the DR related hazard factors with more precision means that we will be able to conduct early avoidance strategies for diabetic retinopathy in the most at-risk populations. The purpose of this investigation is to investigate and analyse the many predictive mechanisms for diabetic retinopathy (DR) in diabetes mellitus using data mining approaches such as support vector machines, decision trees, artificial neural networks, and logistic regressions, among others.