This article introduces an innovative mobile solution for Pterygium detection, an eye disease, using a classification model based on the convolutional neural network (CNN) architecture ResNext50 in images of the anterior segment of the eye. Four models (ResNext50, ResNet50, MobileNet v2, and DenseNet201) were used for the analysis, with ResNext50 standing out for its high accuracy and diagnostic efficiency. The research, focused on applications for ophthalmological medical centers in Lima, Peru, explains the process of development and integration of the ResNext50 model into a mobile application. The results indicate the high effectiveness of the system, highlighting its high precision, recall, and specificity, which exceed 85%, thus showing its potential as an advanced diagnostic tool in ophthalmology. This system represents a significant tool in ophthalmology, especially for areas with ...

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.