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A Hybrid Classifier for Mass Classification with Different Kinds of Features in Mammography

  • Ping Zhang
  • Kuldeep Kumar
  • Brijesh Verma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier’s output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.

Keywords

Feature Selection Hybrid System Classification Rate Digital Mammography Digital Mammogram 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ping Zhang
    • 1
  • Kuldeep Kumar
    • 1
  • Brijesh Verma
    • 2
  1. 1.Faculty of Information TechnologyBond UniversityGold CoastAustralia
  2. 2.Faculty of Informatics & Comm.Central Queensland UniversityRockhamptonAustralia

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