Deep neural network model for diagnosing diabetic retinopathy detection: An efficient mechanism for diabetic management

Abstract

Diabetic retinopathy (DR) is a common eye disease and a notable starting point of blindness in diabetic patients. Detecting the existence of a microaneurysm in the fundus images and the identification of DR in the preliminary stage has been a considerable question for decades. Systematic screening and interference are the most efficient mechanisms for disease management. The sizeable populations of diabetic patients and their enormous screening requirements have given rise to the computer-aided and automatic diagnosis of DR. The utilization of Deep Neural Networks in DR diagnosis has also attracted much attention and considerable advancement has been made. Diabetic retinopathy (DR) includes sensitivity and specificity that are particular to the Diabetic tested (see section on probabilistic reasoning). Even if a test is performed correctly, there is a chance for a false positive or false negative result. However, despite the several advancements that have been made, there remains room for improvement in the sensitivity and specificity of the DR diagnosis. In this work, a novel method called the Luminosity Normalized Symmetric Deep Convolute Tubular Classifier (LN-SDCTC) for DR detection is proposed. The LN-SDCTC method is split into two parts. Initially, with the retinal color fundus images as input, the Luminosity Normalized Retinal Color Fundus Preprocessing model applies to produce a noise-minimized enhanced contrast image. Second, we provide the get-processed image as input to the Symmetric Deep Convolute network. Here, with the aid of the convolutional layer (i.e., the Tubular Neighborhood Window), the average pooling layer (i.e., average magnitude value of tubular neighbors), and the max-pooling layer (i.e., maximum contrast orientation), relevant features are selected. Finally, with the extracted features as input and with the aid of the Multinomial Regression Classification function, the severity of the DR disease is determined. Extensive experimental results in terms of peak signal-to-noise ratio, disease detection time, sensitivity, and specificity reveal that the proposed method of DR detection greatly facilitates the deep learning model and yields better results than various state-of-art methods.