Cervical Cancer Detection Using SVM Based Feature Screening

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Cervical Cancer Detection Using SVM Based Feature Screening
We present a novel feature screening algorithm by deriving relevance measures from the decision boundary of Support Vector Machines. It alleviates the "independence" assumption of traditional screening methods, e.g. those based on Information Gain and Augmented Variance Ratio, without sacrificing computational efficiency. We applied the proposed method to a bottom-up approach for automatic cervical cancer detection in multispectral microscopic thin PAP smear images. An initial set of around 4,000 multispectral texture features is effectively reduced to a computationally manageable size. The experimental results show significant improvements in pixel-level classification accuracy compared to traditional screening methods.
Jiayong Zhang, Yanxi Liu
Added 15 Nov 2009
Updated 15 Nov 2009
Type Conference
Year 2004
Authors Jiayong Zhang, Yanxi Liu
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