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Review

Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

1
Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2
Department of Computer Engineering, University of Engineering and Management, Kolkata 700 156, India
3
Department of Optometry, Medical Research Foundation, Chennai 600 006, India
*
Author to whom correspondence should be addressed.
Academic Editor: Reyer Zwiggelaar
J. Imaging 2021, 7(9), 165; https://doi.org/10.3390/jimaging7090165
Received: 30 June 2021 / Revised: 23 August 2021 / Accepted: 24 August 2021 / Published: 27 August 2021
(This article belongs to the Special Issue Frontiers in Retinal Image Processing)
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented. View Full-Text
Keywords: diabetic retinopathy; artificial intelligence; deep learning; machine-learning; datasets; fundus image; optical coherence tomography; ophthalmology diabetic retinopathy; artificial intelligence; deep learning; machine-learning; datasets; fundus image; optical coherence tomography; ophthalmology
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MDPI and ACS Style

Lakshminarayanan, V.; Kheradfallah, H.; Sarkar, A.; Jothi Balaji, J. Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. J. Imaging 2021, 7, 165. https://doi.org/10.3390/jimaging7090165

AMA Style

Lakshminarayanan V, Kheradfallah H, Sarkar A, Jothi Balaji J. Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. Journal of Imaging. 2021; 7(9):165. https://doi.org/10.3390/jimaging7090165

Chicago/Turabian Style

Lakshminarayanan, Vasudevan, Hoda Kheradfallah, Arya Sarkar, and Janarthanam Jothi Balaji. 2021. "Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey" Journal of Imaging 7, no. 9: 165. https://doi.org/10.3390/jimaging7090165

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