Diabetic Retinopathy Classification Using Hybrid Deep Learning Approach

• Published in: SN computer science 2022-07, Vol.3 (5), p.357, Article 357
• Main Authors: Menaouer, Brahami, Dermane, Zoulikha, El Houda Kebir, Nour, Matta, Nada
• Format: Article
• Language: English
• Subjects: Abnormalities; Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Blood vessels; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Deep learning; Diabetes; Diabetic retinopathy; Information Systems and Communication Service; Machine learning; Medical research; Neural networks; Operations Research; Original Research; Pattern Recognition and Graphics; Retina; Software Engineering/Programming and Operating Systems; Vision
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01240-8

During the recent years, diabetic retinopathy (DR) has been one of the most threatening complications of diabetes that leads to permanent blindness. Further, DR mutilates the retinal blood vessels of a patient having diabetes. Accordingly, various artificial intelligence techniques and deep learning have been proposed to automatically detect abnormalities in DR and its different stages from retina images. In this paper, we propose a hybrid deep learning approach using deep convolutional neural network (CNN) method and two VGG network models (VGG16 and VGG19) to diabetic retinopathy detection and classification according to the visual risk linked to the severity of retinal ischemia. Indeed, the classification of DR deals with understanding the images and their context with respect to the categories. The experimental results, performed on 5584 images, which are an ensemble of online datasets, yielded an accuracy of 90.60%, recall of 95% and F 1 score of 94%. The main aim of this work is to develop a robust system for detecting and classifying DR automatically.