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Volume 14
Issue 18
10.3390/diagnostics14182026
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Article

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

by
Maria Isabel Moreno-Lozano
1,
Edward Jordy Ticlavilca-Inche
2,
Pedro Castañeda
1,*,
Sandra Wong-Durand
1,
David Mauricio
3 and
Alejandra Oñate-Andino
4
1
Information Systems Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
2
Software Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru
3
Systems Engineering and Informatic Faculty, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru
4
Informatic and Electronics Faculty, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba 060155, Ecuador
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(18), 2026; https://doi.org/10.3390/diagnostics14182026
Submission received: 20 August 2024 / Revised: 9 September 2024 / Accepted: 10 September 2024 / Published: 13 September 2024
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

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.
Keywords: pterygium detection; deep learning; Se-ResNext50; ResNext50; ResNet101; ResNext101; MobileNetV2 pterygium detection; deep learning; Se-ResNext50; ResNext50; ResNet101; ResNext101; MobileNetV2

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MDPI and ACS Style

Moreno-Lozano, M.I.; Ticlavilca-Inche, E.J.; Castañeda, P.; Wong-Durand, S.; Mauricio, D.; Oñate-Andino, A. A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images. Diagnostics 2024, 14, 2026. https://doi.org/10.3390/diagnostics14182026

AMA Style

Moreno-Lozano MI, Ticlavilca-Inche EJ, Castañeda P, Wong-Durand S, Mauricio D, Oñate-Andino A. A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images. Diagnostics. 2024; 14(18):2026. https://doi.org/10.3390/diagnostics14182026

Chicago/Turabian Style

Moreno-Lozano, Maria Isabel, Edward Jordy Ticlavilca-Inche, Pedro Castañeda, Sandra Wong-Durand, David Mauricio, and Alejandra Oñate-Andino. 2024. "A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images" Diagnostics 14, no. 18: 2026. https://doi.org/10.3390/diagnostics14182026

APA Style

Moreno-Lozano, M. I., Ticlavilca-Inche, E. J., Castañeda, P., Wong-Durand, S., Mauricio, D., & Oñate-Andino, A. (2024). A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images. Diagnostics, 14(18), 2026. https://doi.org/10.3390/diagnostics14182026

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