Title: Comparative performance of deep learning architectures in classification of diabetic retinopathy

Authors: S. Hari Krishnan; Charen Vishwa; M. Suchetha; Akshay Raman; Rajiv Raman; S. Sehastrajit; D. Edwin Dhas

Addresses: School of Electronics Engineering, Vellore Institute of Technology, Chennai, India ' School of Electronics Engineering, Vellore Institute of Technology, Chennai, India ' Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology, Chennai, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India ' Shri Bhagwan Mahavir Vitreo-Retinal Services, Sankara Nethralaya, Chennai, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India ' School of Electronics Engineering, Vellore Institute of Technology, Chennai, India

Abstract: This paper analyses the performance of deep learning architecture for classifying the retinal fundus images on diabetic retinopathy (DR) and tracing the severity levels of it. Presently, for classifying these fundus images, many deep learning models are employed with the help of several classifiers. The drawback of several deep learning systems is less efficient output, even in some cases, the wrong classification can be encountered. Since this is medical image classification, utmost care and response have to be given to ensure the proper and exact classification without much complexity. This paper aims to analyse the classification performance of different deep learning architectures with respect to the classification of DR severity levels. From these studies, the concept of CNN algorithms, other transfer learning approaches, and CNN-based models with their dedicated usage for image classification applications especially in retinal fundus image classification was analysed. We have utilised the IDRiD challenge dataset and a custom dataset from a leading hospital to demonstrate image classification using different deep-learning architectures.

Keywords: deep learning; convolutional neural network; CNN; image processing; retinal fundus images; diabetic retinopathy.

DOI: 10.1504/IJAHUC.2023.133449

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.44 No.1, pp.23 - 35

Received: 14 Oct 2022
Accepted: 20 Feb 2023
Published online: 15 Sep 2023 *

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