Deep learning model using classification for diabetic retinopathy detection: an overview

Abstract

AbstractEarly detection of diabetic retinopathy is a serious disease for diabetics to minimize their sightlessness risks. The different approaches take a much longer time for a very large training dataset. In classifying to better the accuracy of diabetic retinopathy, a novel technique called MAP Concordance Regressive Camargo’s Index-Based Deep Multilayer Perceptive Learning Classification (MAPCRCI-DMPLC) has been introduced with minimum time consumption. The novel model of MAPCRCI-DMPLC comprises the input layer, hidden layers, and output layer for detecting diabetic retinopathy at an early stage through high accuracy and less moment consumption. The proposed MAPCRCI-DMPLC model collected the retinal fundus images from the dataset as input. After that, we carried out image preprocessing using the MAP-estimated local region filtering-based preprocessing technique in the first hidden layer. In the second hidden layer, Camargo’s index-based ROI extraction is performed to identify the infected region. Then, Concordance Correlative Regression is applied for texture feature extraction. Then the color feature is extracted, beginning the image. The features extracted to the output layer to classify the different levels of DR using the swish activation function through higher accuracy. An investigational assessment using a retinal image dataset on factors such as peak signal-to-noise ratio (PSNR), disease detection accuracy (DDA), false-positive rate (FPR), and disease detection time (DDT), regarding the quantity of retinal fundus images and image dimension. The quantitative and qualitatively analyzed outcome shows a better presentation of our proposed MAPCRCI-DMPLC technique when compared through the five state-of-the-art approaches.