Comparison of pleomorphic and structural features used for breast cancer malignancy classification

Authors:
Łukasz Jeleń;Adam Krzyżak;Thomas Fevens
Affiliations:
Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada
Venue:
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Year:
2008

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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.