A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images
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Authors
Moreno-Lozano, Maria IsabelTiclavilca-Inche, Edward Jordy
Castañeda, Pedro
Wong-Durand, Sandra
Mauricio, David
Oñate-Andino, Alejandra
Issue Date
2024-09-01
Metadata
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DiagnosticsDOI
10.3390/diagnostics14182026Abstract
In this article, various convolutional neural network (CNN) architectures for the detection of pterygium in the anterior segment of the eye are explored and compared. Five CNN architectures (ResNet101, ResNext101, Se-ResNext50, ResNext50, and MobileNet V2) are evaluated with the objective of identifying one that surpasses the precision and diagnostic efficacy of the current existing solutions. The results show that the Se-ResNext50 architecture offers the best overall performance in terms of precision, recall, and accuracy, with values of 93%, 92%, and 92%, respectively, for these metrics. These results demonstrate its potential to enhance diagnostic tools in ophthalmology.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 International
Language
engEISSN
20754418ae974a485f413a2113503eed53cd6c53
10.3390/diagnostics14182026
Scopus Count
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The following license files are associated with this item:
- Creative Commons