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  • Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method

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    Alonso-Caneiro958640-Published.pdf (885.6Kb)
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    Version of Record (VoR)
    Author(s)
    Sulot, D
    Alonso-Caneiro, D
    Ksieniewicz, P
    Krzyzanowska-Berkowska, P
    Iskander, DR
    Griffith University Author(s)
    Alonso-Caneiro, David
    Year published
    2021
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    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 ...
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    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 taskspecific 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. Copyright:
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    Journal Title
    PLoS One
    Volume
    16
    Issue
    6 June
    DOI
    https://doi.org/10.1371/journal.pone.0252339
    Copyright Statement
    © 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.
    Subject
    Biomedical engineering
    Publication URI
    http://hdl.handle.net/10072/412516
    Collection
    • Journal articles

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