Abstract
Classifier plays an important role in a system detecting abnormal shadows from mammograms. In this paper, we propose the novel classification system that cascades four weak classifiers and a classifier ensemble to improve both computational cost and classification accuracy. The first several weak classifiers eliminate a large number of false positives in a short time which are easy to distinguish from abnormal regions, and the final classifier ensemble focuses on the remaining candidate regions difficult to classify, which results in high accuracy. We also show the experimental results using 2,564 mammograms.
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© 2006 Springer-Verlag Berlin Heidelberg
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Nemoto, M., Shimizu, A., Kobatake, H., Takeo, H., Nawano, S. (2006). Study on Cascade Classification in Abnormal Shadow Detection for Mammograms. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_44
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DOI: https://doi.org/10.1007/11783237_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35625-7
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