Open AccessGlaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
Author(s) -
Dominika Sułot,
David AlonsoCaneiro,
Paweł Ksieniewicz,
Patrycja Krzyżanowska-Berkowska,
D. Robert Iskander
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0252339
Subject(s) - optical coherence tomography , glaucoma , artificial intelligence , computer science , convolutional neural network , nerve fiber layer , ensemble learning , deep learning , optic nerve , pattern recognition (psychology) , optic disk , support vector machine , intraocular pressure , ophthalmology , medicine
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.