Title: A review on automated detection and classification of diseases in retinal images

Authors: M.C. Padma; Esra'a Mahmoud Jamil Al Sariera; Thamer Mitib Ahmad Al Sariera

Addresses: Department of Computer Science and Engineering, PES College of Engineering Mandya, India ' Department of Computer Science and Engineering, PES College of Engineering Mandya, India ' Faculty of Computer Science and Informatics, Amman Arab University, Amman, Jordan

Abstract: Hypertensive retinopathy (HR) and glaucoma are the most diseases that cause blindness. Early detection of these diseases is very important for ophthalmologists in clinical diagnostic and successful treatment. The identification and diagnosis of glaucoma and HR require segmentation of the normal objects inside retina such as blood vessels and optic disc (OD). This article describes publicly available retinal datasets and an overview of the state-of-the-art for segmenting normal objects in the retina such as blood vessels and the OD, as well as ways for detecting pathologies that affect normal objects in the retina such as glaucoma and HR. The purpose of this study is to develop a professional structure that will familiarise the researcher with the most up-to-date blood vessels and OD segmentation techniques and the classification of HR and glaucoma diseases. Furthermore, we compared the dataset, evaluation metrics, pre-and post-processing steps, segmentation and classification methods and induced results of these approaches.

Keywords: hypertensive retinopathy; HR; glaucoma; blood vessels; optic disc; OD.

DOI: 10.1504/IJMEI.2024.140802

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.5, pp.440 - 454

Received: 27 Dec 2021
Accepted: 15 May 2022
Published online: 03 Sep 2024 *

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