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Mammogram Problem Solving Approach: Building CBR Classifier for Classification of Masses in Mammogram

  • Valliappan Raman
  • Patrick Then
  • Putra Sumari
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)

Abstract

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. We study the application of these algorithms as a classification system in order to differentiate benign from malignant mass, obtained from MIAS database, and we compare the classifier with other classification techniques to test the accuracy.

Keywords

Mammography Segmentation CaseBase Classification 

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Valliappan Raman
    • 1
  • Patrick Then
    • 1
  • Putra Sumari
    • 2
  1. 1.Swinburne University of Technology SarawakKuchingMalaysia
  2. 2.University Sains MalaysiaPenangMalaysia

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