Abstract
In this paper, we propose a novel combinational SVM algorithm via a set of decision rules to achieve better performances in microcalcification detection inside digital mammograms towards computer aided breast cancer diagnosis. Based on the discovery that the polynomial SVM is sensitive to MC (microcalcification) pixels and the linear SVM is sensitive to non-MC pixels, we designed an adaptive threshold mechanism via establishment of their correspondences to exploit the complementary nature between the polynomial SVM and the linear SVM. Experiments show that the proposed algorithm successfully reduced false positive detection rate while keeping the true positive detection rate competitive.
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Li, Y., Jiang, J. (2004). Combination of SVM Knowledge for Microcalcification Detection in Digital Mammograms. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_53
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DOI: https://doi.org/10.1007/978-3-540-28651-6_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
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