Mobile Application Based on Convolutional Neural Networks for Pterygium Detection in Anterior Segment Eye Images at Ophthalmological Medical Centers

Edward Jordy Ticlavilca-Inche, Maria Isabel Moreno-Lozano, Pedro Castañeda, Sandra Wong-Durand, Alejandra Oñate-Andino
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Abstract

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 limited access to specialists, offering a rapid and reliable diagnosis of Pterygium. The study also addresses the technical challenges and clinical implications of implementing this technology in a real-world context.
基于卷积神经网络的移动应用程序,用于在眼科医疗中心的眼球前段图像中检测翼状胬肉
本文介绍了一种创新的移动解决方案,该方案采用基于卷积神经网络(CNN)架构 ResNext50 的分类模型,对眼球前段图像进行翼状胬肉检测。分析中使用了四种模型(ResNext50、ResNet50、MobileNet v2 和 DenseNet201),其中 ResNext50 以其高精度和诊断效率脱颖而出。该研究侧重于秘鲁利马眼科医疗中心的应用,解释了将 ResNext50 模型开发和集成到移动应用中的过程。研究结果表明,该系统具有很高的有效性,其精确度、召回率和特异性都超过了 85%,从而显示了其作为眼科先进诊断工具的潜力。该系统是眼科领域的一个重要工具,尤其是在专家资源有限的地区,它能快速可靠地诊断翼状胬肉。这项研究还探讨了在现实世界中应用这项技术所面临的技术挑战和临床影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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