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Proceedings Paper

Abnormal cervical cell detection based on an adaptive margin-based feature selection method
Author(s): Lili Zhao; Kuan Li; Hongyun Yang; Jianping Yin
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Paper Abstract

In an abnormal cervical cell detection system the discriminated abilities of different features are not same so the optimized combination method of all features is an essential component to this system. Feature selection can improve each feature utilization ratio and the performance of the classification problem. The previous efforts of cervical abnormal cell detection are mainly focused on changing feature space into a new one by using a binary weight vector. In this work, the binary weight values are extended to the multiple weight values. According to the statistical distribution situation of the data, an adaptive margin-based weighted feature selection method is proposed in this paper. This method performs best compared with the other 3 methods. The experimental result achieves 96% accuracy in a real-world cervical smear image dataset.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411Y (17 March 2017); doi: 10.1117/12.2268406
Show Author Affiliations
Lili Zhao, National Univ. of Defense Technology (China)
Kuan Li, National Univ. of Defense Technology (China)
Hongyun Yang, National Univ. of Defense Technology (China)
Jianping Yin, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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