Adaptive splitting and selection algorithm for classification of breast cytology images

Authors:
Bartosz Krawczyk;Paweł Filipczuk;Michał Woźniak
Affiliations:
Department of Systems and Computer Networks, Wroclaw University of Technology, Wrocław, Poland;Institute of Control & Computation Engineering, University of Zielona Góra, Zielona Góra, Poland;Department of Systems and Computer Networks, Wroclaw University of Technology, Wrocław, Poland
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Year:
2012

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Abstract

The article presents an application of Adaptive Splitting and Selection (AdaSS) classifier in the medical decision support system for breast cancer diagnosis. Apart from the canonical malignant versus non-malignant problem we introduced a third class - fibroadenoma, which is a benign tumor of the breast often occurring in women. Medical images are delivered by the Regional Hospital in Zielona Góra, Poland. For the process of segmentation and feature extraction a mixture of Gaussians is used. AdaSS is a combined classifier, based on an evolutionary splitting of feature space into clusters. To increase the overall accuracy of the classification we propose to add a feature selection step to the optimization criterion of the native AdaSS algorithm. Experimental investigation proves that the introduced method is more accurate than previously used classification approaches.