Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms

Authors:
Tomasz Arod;Marcin Kurdziel;Erik O. D. Sevre;David A. Yuen
Affiliations:
Institute of Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Kraków, Poland;Institute of Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Kraków, Poland;Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA;Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2005

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Abstract

We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as well. In order to achieve a higher accuracy for this method, we should choose image features that are better suited for analysing digital mammograms than the currently used ones.