Deep Learning Image Analysis of Macular Optical Coherence Tomography Angiography Images for Detection of Progression in Glaucoma-Reference-Cited by-同舟云学术

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Deep Learning Image Analysis of Macular Optical Coherence Tomography Angiography Images for Detection of Progression in Glaucoma

Published:2024-02-05 Issue: Volume:63 Page:130-136
ISSN:
Container-title:2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)
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Author:

Mousavian Aylin¹,Jarkaneh Jabiz²,Pourhomayoun Mohammad¹,Kaur Manveen¹,Chatterjee Rohan¹,Besharati Sajad³,Nouri-Mahdavi Kouros³,Amini Navid¹

Affiliation:

1. California State University, Los Angeles,Department of Computer Science,Los Angeles,CA,USA

2. University of the West of England,Department of Public Health,Bristol,England,UK

3. Stein Eye Institute, University of California, Los Angeles,Los Angeles,CA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10504287/10504309/10504332.pdf?arnumber=10504332

Reference52 articles.

1. Influence of the Disc–Fovea Angle on Limits of RNFL Variability and Glaucoma Discrimination

2. ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

3. Influence of Correction of Ocular Magnification on Spectral-Domain OCT Retinal Nerve Fiber Layer Measurement Variability and Performance

4. Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma

5. Detection of Glaucoma from Fundus Images Using Novel Evolutionary-Based Deep Neural Network

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