Adjustment of Convolutional and Hidden Layers Using Type-1 Fuzzy Logic Applied to Diabetic Retinopathy Classification

Abstract

Vision problems are common in patients with diabetes mellitus (DM) because they may suffer from diabetic retinopathy (DR). Because the symptoms of this condition are not easy to detect without the intervention of an expert technician, the use of convolutional neural networks (CNN) has been implemented to speed up the process of analyzing retina images. Due to the good results of this technology, efforts have been made to combine it with other technologies. In this paper, we present the use of an intelligent hybrid system that uses CNNs and Fuzzy Logic with the aim of improving the accuracy obtained. The implementation of fuzzy logic to adjust the hyperparameters of the network allowed us to obtain a mean of 0.9526 with a standard deviation of 0.008521158 in the binary case study, while in the multiclass case study we obtained a mean of 0.7299 and a standard deviation of 0.015614013, offering better results when fuzzy logic is combined compared to when not.