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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Nemoto, M., Shimizu, A., Kobatake, H., Takeo, H., & Nawano, S. (2006). Study on cascade classification in abnormal shadow detection for mammograms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4046 LNCS, pp. 324–331). Springer Verlag. https://doi.org/10.1007/11783237_44
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