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A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment
Maria Isabel Moreno-Lozano1, Edward Jordy Ticlavilca-Inche2, Pedro Castañeda1
1Information Systems Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru.
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.
