A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Abstract

Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild, moderate, severe, no PDR, PDR in DR. Initially, saliency detection is applied on color images to detect maximum salient foreground objects from the background. Next, structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object. Finally, active contours approximation is performed using gradient descent to segment the lesions from the images. Afterwards, the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild, moderate, severe, No PDR and PDR. Based on the computed ratio obtained from segmented images, the severity levels were identified. Meanwhile, statistical parameters like the mean and the standard deviation of pixel intensities, mean of hue, saturation and deviation clustering, are estimated through K-means, which are computed as features from the output images of the proposed segmentation process. Using these derived feature sets as input to the classifier, the classification of DR was performed. Finally, a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total. The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82% and an accuracy of 96%. The proposed system was able to label and classify DR grades automatically.

RETRACTED: ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation

“…VGG stands for Visual Geometry Group, an academic group at Oxford University. This team presented two famous networks: VGG-16 (Jahangeer and Rajkumar, 2021) and VGG-19 (Sudha and Ganeshbabu, 2021), which are included as library packages of popular programming languages such as Python and MATLAB. This study chooses VGG-16 because it is easier to implement and has less layers, while VGG-16 has similar performance of VGG-19.…”

Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier

“…( Qummar et al, 2019 ) used an ensemble of Resnet50, Inceptionv3, Xception, Dense121, and Dense169 deep network models. ( Sudha & Ganeshbabu, 2021 ) adopted the VGG-19 model combined with structure tensor for enhancing local patterns of edge elements and active contours approximation for lesion segmentation. ( Vaishnavi, Ravi & Anbarasi, 2020 ) used Contrast-limited adaptive histogram equalization (CLAHE) model and AlexNet network architecture with SoftMax layer for classification.…”

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

“…The final fully connected layer gathers all features descriptors and flattens every matrix map into a vector. The VGG [21] group has utilized six deep convolutional neural networks out of which VGG16 and VGG19 models were found successful. VGG used depth as most prominent parameter to evaluate the network suitable for best recognition and accuracy in its classification of convolutional neural networks.…”