Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,...

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Veröffentlicht in:	Scientific reports Jg. 8; H. 1; S. 14665 - 9
Hauptverfasser:	Shibata, Naoto, Tanito, Masaki, Mitsuhashi, Keita, Fujino, Yuri, Matsuura, Masato, Murata, Hiroshi, Asaoka, Ryo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 02.10.2018 Nature Publishing Group
Schlagworte:	692/308 692/308/575 Algorithms Color Datasets Deep learning Glaucoma Humanities and Social Sciences multidisciplinary Photography Science Science (multidisciplinary) Training Weights & measures Glaucomatous Indications Deep Residual Learning Fundus Photographs Area Under The Receiver Operating Characteristic Curve (AROC) ResNet
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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