Identification of diabetic retinopathy using deep learning algorithm and blood vessel extraction

Abstract

Retinal blood vessel and retinal vessel tree segmentation are significant components in disease identification systems. Diabetic retinopathy is found using identifying hemorrhages in blood vessels. The debauched vessel segmentation helps in an image segmentation process to improve the accuracy of the system. This paper uses Edge Enhancement and Edge Detection method for blood vessel extraction. It covers drusen, exudates, vessel contrasts and artifacts. After extracting the blood vessel, the dataset is fed into CNN network called EyeNet for identifying DR infected images. It is observed that EyeNet leads to Sensitivity of about 90.02%, Specificity of about 98.77% and Accuracy of about 96.08%. Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, India *Author for correspondence: Email-gananthi@tce.edu Introduction Diabetic Retinopathy is a diabetic complication that affect eye. The automated system was developed for suitable detection of the disease using fundus image and segmentation [1]. The location of anomalies in fovea is being identified and helpful for diagnosis. The detection of retinal parts was carried out as part of the overall device growth, and the results have been published. The method of removing the usual retinal components: blood vessels, fovea, and optic disc, allows for the identification of lesions. There are different techniques explained in for blood vessel extraction namely Edge Enhancement and Edge Detection, Modified Matched Filtering, Continuation Algorithm and Image Line Cross Section. Diabetic retinopathy is a serious eye disorder that can lead to blindness in people of working age [2,3]. A multilayer neural network with three primary color components of the image, namely red, green, and blue as inputs, is used to identify and segment retinal blood vessels. The back propagation algorithm is used, which provides a reliable method for changing the weights in a feed-forward network. Deep convolutional neural networks have recently demonstrated superior image classification efficiency as compared to the feature-extracted image classification methods [4]. Authors proposed morphological processing, thresholding, edge detection, and adaptive histogram equalization to segment and extract blood vessels from retinal images [5].