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Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

H. Asha Gnana Priya1, J. Anitha1, Daniela Elena Popescu2, Anju Asokan1, D. Jude Hemanth1, Le Hoang Son3,4,*
1 Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
2 Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania
3 Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
4 VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam
* Corresponding Author: Le Hoang Son. Email:
(This article belongs to this Special Issue: Deep Learning Trends in Intelligent Systems)

Computers, Materials & Continua 2021, 66(3), 2771-2786.

Received 17 July 2020; Accepted 15 August 2020; Issue published 28 December 2020


Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable alternative for screening and grading of DR for a larger population. This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques. The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.


Diabetic retinopathy; computer-aided diagnosis system; vessel extraction; optic disc segmentation; retinal features; grading of DR

Cite This Article

H. Asha Gnana Priya, J. Anitha, D. Elena Popescu, A. Asokan, D. Jude Hemanth et al., "Detection and grading of diabetic retinopathy in retinal images using deep intelligent systems: a comprehensive review," Computers, Materials & Continua, vol. 66, no.3, pp. 2771–2786, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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