Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus-Reference-Cited by-同舟云学术

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Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus

Published:2020-04-24 Issue:2 Volume:9 Page:24
ISSN:2164-2591
Container-title:Translational Vision Science & Technology
language:en
Short-container-title:Trans. Vis. Sci. Tech.

Author:

Cao Ke¹²,Verspoor Karin³,Sahebjada Srujana¹²,Baird Paul N.²

Affiliation:

1. Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia

2. Department of Surgery, Ophthalmology, The University of Melbourne, Melbourne, Victoria, Australia

3. Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Subject

Ophthalmology,Biomedical Engineering

Cited by 43 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices;Eye and Vision;2024-07-08

2. Performance Comparison of Machine Learning Algorithms for Keratoconus Detection;2024 IEEE 30th International Conference on Telecommunications (ICT);2024-06-24

3. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review;Frontiers in Ophthalmology;2024-05-17

4. Diagnosis of Forme Fruste Keratoconus Using Corvis ST Sequences with Digital Image Correlation and Machine Learning;Bioengineering;2024-04-26

5. Artificial intelligence as diagnostic modality for keratoconus: A systematic review and meta-analysis;Journal of Taibah University Medical Sciences;2024-04

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