SVM-based automatic diagnosis method for keratoconus

Yuhong Gao; Qiang Wu; Jing Li; Jiande Sun; Wenbo Wan

doi:10.1117/12.2280344

19 June 2017 SVM-based automatic diagnosis method for keratoconus

Yuhong Gao, Qiang Wu, Jing Li, Jiande Sun, Wenbo Wan

Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 104430Z (2017) https://doi.org/10.1117/12.2280344
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore

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.

Citation Download Citation

Yuhong Gao, Qiang Wu, Jing Li, Jiande Sun, and Wenbo Wan "SVM-based automatic diagnosis method for keratoconus", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104430Z (19 June 2017); https://doi.org/10.1117/12.2280344

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