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Journal of Healthcare Engineering
Volume 2018, Article ID 6874765, 8 pages
https://doi.org/10.1155/2018/6874765
Research Article

Comparison of Machine-Learning Classification Models for Glaucoma Management

1R&D Division, Topcon Corporation, Tokyo, Japan
2Cloud-Based Eye Disease Diagnosis Joint Research Team, RIKEN Center for Advanced Photonics, RIKEN, Wako, Japan
3Tohoku University Graduate School of Medicine, Sendai, Japan
4Image Processing Research Team, RIKEN Center for Advanced Photonics, RIKEN, Wako, Japan

Correspondence should be addressed to Masahiro Akiba; pj.oc.nocpot@abika-m

Received 27 December 2017; Revised 6 April 2018; Accepted 18 April 2018; Published 19 June 2018

Academic Editor: Altaf Hussain

Copyright © 2018 Guangzhou An et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Sahil Thakur, “Technology in the Making and the Future of Ophthalmology,” Current Advances in Ophthalmic Technology, pp. 153–160, 2019. View at Publisher · View at Google Scholar
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  • Chengjie Zheng, Thomas V. Johnson, Aakriti Garg, and Michael V. Boland, “Artificial intelligence in glaucoma,” Current Opinion in Ophthalmology, vol. 30, no. 2, pp. 97–103, 2019. View at Publisher · View at Google Scholar
  • Siamak Yousefi, Michael Boland, Tobias Elze, and Louis R. Pasquale, “Glaucoma Monitoring Using Manifold Learning and Unsupervised Clustering,” International Conference Image and Vision Computing New Zealand, vol. 2018-, 2019. View at Publisher · View at Google Scholar