This paper is in the following e-collection/theme issue:

Digital Health Reviews (1331) Machine Learning (1505) Imaging Informatics (202) Ophthalmology (48) Artificial Intelligence (1430) Computer-Aided Diagnosis (136)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27414, first published .
Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis

Authors of this article:

Ali Q Saeed1, 2 Author Orcid Image ;   Siti Norul Huda Sheikh Abdullah1 Author Orcid Image ;   Jemaima Che-Hamzah3 Author Orcid Image ;   Ahmad Tarmizi Abdul Ghani1 Author Orcid Image
Article Authors Cited by (21) Tweetations (2) Metrics

Ali Q Saeed   1, 2 * , MSc ;   Siti Norul Huda Sheikh Abdullah   1 * , PhD ;   Jemaima Che-Hamzah   3 * , PhD ;   Ahmad Tarmizi Abdul Ghani   1 * , PhD

1 Center for Cyber Security, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia

2 Computer Center, Northern Technical University, Ninevah, Iraq

3 Department of Ophthalmology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur, Malaysia

*all authors contributed equally

Corresponding Author:

  • Ali Q Saeed, MSc
  • Center for Cyber Security, Faculty of Information Science & Technology
  • Universiti Kebangsaan Malaysia
  • Bangi Street, Bangi
  • Selangor, 43600
  • Malaysia
  • Phone: 60 7740870504
  • Email: ali.qasim@ntu.edu.iq