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.
Bibliographical noteFunding Information:
We thank Professor Robert Hollebeck, University of Pennsylvania, for his encouragement and suggestions and Dr. T. Popiela from Department of Radiology, Collegium Medicum, Jagiellonian University, Krakow (Poland) for medical consultations. We thank Ben Holtzmann and Lilli Yang for contributing to Fig. 1. This research has been supported by the Math-Geo program of National Science Foundation and the Digital Technology Center of Univ. Minnesota.
Copyright 2008 Elsevier B.V., All rights reserved.
- Computer-aided diagnosis
- Machine learning
- Mammogram analysis