Automated Diabetic Retinopathy detection and classification using stochastic coordinate descent deep learning architectures

Abstract

A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, the layer-by-layer comparison of convolution matrix, pooling, transition, and dense of each network along with their matrix order is examined. Loss minimization after the testing data from the prediction analysis is considered as objective function for every stage of convolution networks. Similarity among the networks is identified for optimizing the matrix order of each layer and the minimization is performed for classifying the Diabetic Retinopathy level. The performance of the proposed optimization architectures confirmed through confusion matrix for every data set taking training accuracy and data loss as measures. The result of the proposed optimized schemes shows that for different the datasets different in amount data displays the dominance than existing schemes. The numerical analysis of different training accuracy and data loss is presented for validating the proposed work.