A Review of Deep Learning Techniques for Glaucoma Detection-Reference-Cited by-同舟云学术

A Review of Deep Learning Techniques for Glaucoma Detection

Published:2023-03-23 Issue:3 Volume:4 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Guergueb Takfarines^ORCID,Akhloufi Moulay A.

Funder

Université de Moncton

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s42979-023-01734-z.pdf

Reference117 articles.

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2. Khalil T, Khalid S, Syed AM. Review of machine learning techniques for glaucoma detection and prediction. In: 2014 science and information conference. 2014; IEEE. p. 438–442.

3. Tham Y-C, Li X, Wong TY, Quigley HA, Aung T, Cheng C-Y. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014;121(11):2081–90.

4. Barros DM, Moura JC, Freire CR, Taleb AC, Valentim RA, Morais PS. Machine learning applied to retinal image processing for glaucoma detection: review and perspective. Biomed Eng Online. 2020;19:1–21.

5. Divya L, Jacob J. Performance analysis of glaucoma detection approaches from fundus images. Proc Comput Sci. 2018;143:544–51.

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