CNNs for automatic glaucoma assessment using fundus images: an extensive validation

Diaz-Pinto, A orcid.org/0000-0002-4865-8296, Morales, S, Naranjo, V et al. (3 more authors) (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. BioMedical Engineering OnLine, 18. 29. ISSN 1475-925X

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Copyright, Publisher and Additional Information: © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keywords: Glaucoma; ACRIMA database; Fundus images; CNN; Fine-tuning
Dates:
  • Published: 20 March 2019
  • Accepted: 13 March 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 18 Sep 2019 10:12
Last Modified: 18 Sep 2019 10:12
Status: Published
Publisher: BMC
Identification Number: https://doi.org/10.1186/s12938-019-0649-y
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