Abstract
Breast cancer is one of the high-risk cancers, and breast ultrasound is routinely used as an adjunct to mammography for detection and diagnosis. Furthermore, the effective computer-aided diagnosis (CAD) system could improve the specificity of discriminating malignant from benign lesions on breast ultrasound images. This paper presents a method for discrimination between benign and malignant breast cancers in ultrasound images based on cost-sensitive boosting. Firstly, the image feature is extracted according to BI-RADS (Breast imaging report and data system), and a more simplified sub-feature set is obtained through minimal redundancy maximal relevance (mRMR) algorithm. Then three cost-sensitive Boosting models are trained and compared, and the optimal classification parameters are obtained by cross validation. Experiment shows that cost-sensitive AdaBoost performs the best, with AUC (area under receive operating characteristic curve) at 0.859 in the condition of controlled FNR (false negative rate) at 5%, better than CS-RealBoost and CS-LogitBoost.
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Shen, X., Zhang, S., Yao, R., Chen, Y., Zhu, YM., Zhang, S. (2012). Discrimination between Benign and Malignant Breast Cancers in Ultrasound Images Based on Cost-Sensitive Boosting. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_18
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DOI: https://doi.org/10.1007/978-3-642-31919-8_18
Publisher Name: Springer, Berlin, Heidelberg
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