A hybrid classifier for mass classification with different kinds of features in mammography

Authors:
Ping Zhang;Kuldeep Kumar;Brijesh Verma
Affiliations:
Faculty of Information Technology, Bond University, Gold Coast, QLD, Australia;Faculty of Information Technology, Bond University, Gold Coast, QLD, Australia;Faculty of Informatics & Comm., Central Queensland University, Rockhampton, QLD, Australia
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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Abstract

This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.