Abnormal cervical cell detection based on an adaptive margin-based feature selection method

Lili Zhao; Kuan Li; Hongyun Yang; Jianping Yin

doi:10.1117/12.2268406

17 March 2017 Abnormal cervical cell detection based on an adaptive margin-based feature selection method

Lili Zhao, Kuan Li, Hongyun Yang, Jianping Yin

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103411Y (2017) https://doi.org/10.1117/12.2268406
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

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.

Citation Download Citation

Lili Zhao, Kuan Li, Hongyun Yang, and Jianping Yin "Abnormal cervical cell detection based on an adaptive margin-based feature selection method", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411Y (17 March 2017); https://doi.org/10.1117/12.2268406

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KEYWORDS

Feature selection

Feature extraction

Image segmentation

Computing systems

Binary data

Cervical cancer

Detection and tracking algorithms

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