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Study on Cascade Classification in Abnormal Shadow Detection for Mammograms

  • Mitsutaka Nemoto
  • Akinobu Shimizu
  • Hidefumi Kobatake
  • Hideya Takeo
  • Shigeru Nawano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4046)

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.

Keywords

Feature Selection Classifier Ensemble Weak Classifier Fuji Photo Film Sequential Forward Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mitsutaka Nemoto
    • 1
  • Akinobu Shimizu
    • 1
  • Hidefumi Kobatake
    • 1
  • Hideya Takeo
    • 2
  • Shigeru Nawano
    • 3
  1. 1.Tokyo University of Agriculture and TechnologyTokyoJapan
  2. 2.Kanagawa Institute of TechnologyKanagawaJapan
  3. 3.National Cancer Center Hospital EastChibaJapan

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