Performance analysis of glaucoma detection techniques

Name: Sahu, Deepti

Organization : Sharda University. CSE Department

Email : deepti.sahu@sharda.ac.in

Name: Kaur, Mandeep

Organization : Sharda University. CSE Department

Email : mandeep.kaur@sharda.ac.in

LCSH: Glaucoma -- Diagnosis

LCSH: Medical imaging

LCSH: Optical coherence tomography

Abstract: Glaucoma is an incurable eye disorder and it is t second foremost reason for the visual deficiency. Commonly it is detected by the use of retina images. It is hard to anticipate glaucoma in beginning phases because the side effects of glaucoma can be identified only, once the sickness reaches at a high-level stage. Therefore normal eve screening fundamental and suggested. The manual method of glaucoma screening is a time-consuming and labor-intensive process. But deep learning-based glaucoma detection approaches diminish the manual effort and improve accuracy and speed. Optic cup to disc ratio is a major clinical pointer for glaucoma finding. For this ratio we need to perform segmentation of optic disc and cup and this can be performed via the segmentation methodologies based on deep learning. This paper presents a survey of different deep learning-based methodologies used for glaucoma detection.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2021

Modified: 2024-06-12

Issued: 2024-06-12

บทความ/Article

application/pdf

BibliograpyCitation : In IEEE Computer Society. 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT 2021) (pp.236-241). Los Alamitos, CA : IEEE Computer Society

eng

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