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Comparison of Pleomorphic and Structural Features Used for Breast Cancer Malignancy Classification

  • Łukasz Jeleń
  • Adam Krzyżak
  • Thomas Fevens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

Abstract

Malignancy of a cancer is one of the most important factors that are taken into consideration during breast cancer. Depending on the malignancy grade the appropriate treatment is suggested. In this paper we make use of the Bloom-Richardson grading system, which is widely used by pathologists when grading breast cancer malignancy. Here we discuss the use of two categories of cells features for malignancy classification. The features are divided into polymorphic features that describe nuclei shapes, and structural features that describe cells ability to form groups. Results presented in this work, show that calculated features present a valuable information about cancer malignancy and they can be used for computerized malignancy grading. To support that argument classification error rates are presented that show the influence of the features on classification. In this paper we compared the performance of Support Vector Machines (SVMs) with three other classifiers. The SVMs presented here are able to assign a malignancy grade based on pre–extracted features with accuracy up to 94.24% for pleomorphic features and with an accuracy 91.33% when structural features were used.

Keywords

malignancy grading FNA grading breast cancer grading Bloom–Richardson features 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Łukasz Jeleń
    • 1
  • Adam Krzyżak
    • 1
  • Thomas Fevens
    • 1
  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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