A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images

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 identif...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Maria Isabel Moreno-Lozano, Edward Jordy Ticlavilca-Inche, Pedro Castañeda, Sandra Wong-Durand, David Mauricio, Alejandra Oñate-Andino
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2024-09-01
Schriftenreihe:	Diagnostics
Schlagworte:	pterygium detection deep learning Se-ResNext50 ResNext50 ResNet101 ResNext101
Online-Zugang:	https://www.mdpi.com/2075-4418/14/18/2026
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