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...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | Perú |
| Institución: | Universidad Peruana de Ciencias Aplicadas |
| Repositorio: | UPC-Institucional |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorioacademico.upc.edu.pe:10757/676288 |
| Acceso en línea: | https://doi.org/10.3390/diagnostics14182026 http://hdl.handle.net/10757/676288 |
| Access Level: | acceso abierto |
| Palabra clave: | deep learning MobileNetV2 pterygium detection ResNet101 ResNext101 ResNext50 Se-ResNext50 https://purl.org/pe-repo/ocde/ford#2.11.00 |
| Sumario: | 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. |
|---|