KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks
KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks
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DOI：
10.1155/2019/8162567
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发表时间：
2019-01-23
影响因子：
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通讯作者：
Valentin, Popa
中科院分区：
文献类型：
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作者：
Lavric, Alexandru;Valentin, Popa
圆锥角膜（KTC）是一种非炎症性疾病，其特征是角膜逐渐变薄，角膜变形和瘢痕形成。这种情况的病理机制已经被研究了很长时间。近年来，这种疾病引起了许多研究中心的注意，因为被诊断患有圆锥角膜的人数正在上升。在这种情况下，迅速需要能够促进诊断和治疗选择的解决方案。本文的主要贡献是实现了一种能够确定眼睛是否受到圆锥角膜影响的算法。， e KeratoDetect算法使用卷积神经网络（CNN）来分析眼睛的角膜地形，该网络能够提取和学习圆锥角膜的特征。结果表明，KeratoDetect算法保证了较高的性能水平，在数据测试集上获得了99.33%的准确率。KeratoDetect可以帮助眼科医生快速筛查患者，从而减少诊断错误，促进治疗。
Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea., e 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., emain contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus., e 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., e 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.