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Proceedings Paper

SVM-based automatic diagnosis method for keratoconus
Author(s): Yuhong Gao; Qiang Wu; Jing Li; Jiande Sun; Wenbo Wan
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Paper Abstract

Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430Z (19 June 2017); doi: 10.1117/12.2280344
Show Author Affiliations
Yuhong Gao, Shandong Univ. (China)
Qiang Wu, Shandong Univ. (China)
Jing Li, Shandong Management Univ. (China)
Jiande Sun, Shandong Normal Univ. (China)
Wenbo Wan, Shandong Normal Univ. (China)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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