Classification of diabetic retinopathy stages based on neural networks

Abstract

Diabetic retinopathy is one of the main side effects of diabetes, which causes severe effects, including blindness. The main challenge is the early diagnosis of this disease for timely and effective treatment. Diabetic retinopathy can be detected much faster and more accurately by using machine learning methods for image analyzing of the human retina. The development of methods and algorithms for the detection and classification of this disease, the automation of this process are the actual and costeffective goals.The article focuses on the classification of the stages of diabetic retinopathy using neural networks based on human retinal images. Classification problem of diabetic retinopathy stages is described.The architecture of deep neural networks based on VGG16 and VGG19 with the addition of custom layers is proposed. Recommendations for the selection of the size of the initial retinal images and the preprocessing stage (cropping) are given As a result of the performed experimental research. Analysis of the dataset was performed. Neural network models were trained and results were evaluated with class imbalance taken into account.