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A Review of Deep Learning Techniques for Glaucoma Detection

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Abstract

Glaucoma is one of the major reasons for visual impairment all across the globe. The recent advancements in machine learning techniques have greatly facilitated ophthalmologists in the early diagnosis of ocular diseases through the employment of automated systems. Several studies have been published lately to address the timely detection of glaucoma using deep learning approaches. A comprehensive review of the deep learning approaches employed for glaucoma detection using retinal fundus images is presented in this paper. The available retinal image datasets, image pre-processing techniques, state-of-the-art models, and performance evaluation metrics used in the recent studies are reviewed. This systematic review aims to provide critical insights and potential research directions to the ophthalmologists and researchers in this domain.

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This work was enabled in part by support provided by the New Brunswick Health Research Foundation (NBHRF), and the New Brunswick Innovation Foundation (NBIF).

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  1. Takfarines Guergueb and Moulay A. Akhloufi have contributed equally to this work.

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  1. Perception, Robotics, and Intelligent Machines Research Group (PRIME), Computer Science Department, Université de Moncton, Moncton, NB, Canada

    Takfarines Guergueb & Moulay A. Akhloufi

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  1. Takfarines Guergueb
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  2. Moulay A. Akhloufi
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Correspondence to Takfarines Guergueb.

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Guergueb, T., Akhloufi, M.A. A Review of Deep Learning Techniques for Glaucoma Detection. SN COMPUT. SCI. 4, 274 (2023). https://doi.org/10.1007/s42979-023-01734-z

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