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Published VersionCC BY V4.0, Open Access
Journal article
Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
PLoS One, Vol.16(6), pp.1-12
2021
PMID: 34086716
Abstract
This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in a low data regime to differentiate glaucoma patients from healthy controls. The two groups of subjects were initially categorized based on a range of clinical tests including measurements of intraocular pressure, visual fields, oct derived retinal nerve fiber layer (rnfl) thickness and dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used. A new task-specific convolutional neural network architecture was developed for slo image-based classification. To benchmark the results of the proposed method, a range of classifiers were tested including five machine learning methods to classify glaucoma based on rnfl thickness-a well-known biomarker in glaucoma diagnostics, ensemble classifier based on inception v3 architecture, and classifiers based on features extracted from the image. The study shows that cross-validation dl ensemble based on slo images achieved a good discrimination performance with up to 0.962 of balanced accuracy, outperforming all of the other tested classifiers.
Details
- Title
- Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
- Authors
- Dominika Sulot (Corresponding Author) - Wrocław University of Science and TechnologyDavid Alonso-Caneiro (Author) - Queensland University of TechnologyPawel Ksieniewicz (Author) - Wrocław University of Science and TechnologyPatrycja Krzyzanowska-Berkowska (Author) - Wroclaw Medical UniversityD. Robert Iskander (Author) - Wrocław University of Science and Technology
- Publication details
- PLoS One, Vol.16(6), pp.1-12
- Publisher
- Public Library of Science
- Date published
- 2021
- DOI
- 10.1371/journal.pone.0252339
- ISSN
- 1932-6203
- PMID
- 34086716
- Copyright note
- © 2021 Sułot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Data Availability
- All relevant data are within the paper and its Supporting information files.
- Grant note
- The work of D. S. was supported by InterDok -- Interdisciplinary Doctoral Studies Projects at Wroclaw University of Science and Technology, a project co-financed by the European Union under the European Social Fund, D.R. I.was supported by the National Science Centre, Poland within the OPUS grant [2018/29/B/ST7/02451], D. A.-C. was supported by Rebecca L. Cooper 2018 Project Grant and the National Health & Medical Research Council Ideas Grant (APP1186915), while P. K., by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99972396502621
- Output Type
- Journal article
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- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Ophthalmology
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Source: InCites