Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, Suceava 720229, Romania.
Comput Intell Neurosci. 2019 Jan 23;2019:8162567. doi: 10.1155/2019/8162567. eCollection 2019.
Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.
圆锥角膜（KTC）是一种非炎症性疾病，其特征是角膜渐进性变薄、变形和瘢痕形成。该疾病的病理机制已经研究了很长时间。近年来，由于诊断为圆锥角膜的人数不断增加，许多研究中心开始关注这种疾病。在这种情况下，人们需要能够快速确定诊断和治疗方案的解决方案。本文的主要贡献是实现了一种能够确定眼睛是否患有圆锥角膜的算法。Kerat oDetect 算法使用卷积神经网络（CNN）分析眼睛的角膜地形，该网络能够提取和学习圆锥角膜眼的特征。结果表明，Kerat oDetect 算法具有很高的性能水平，在数据测试集上获得了 99.33%的准确率。Kerat oDetect 可以帮助眼科医生快速筛选其患者，从而减少诊断错误并促进治疗。
