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Presentation
A hybrid classifier for mass classification with different kinds of features in mammography
Paper presented at the 2nd international conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005)
  • Ping Zhang, Bond University
  • Kuldeep Kumar, Bond University
  • Brijesh Verma
Date of this Version
8-29-2005
Document Type
Conference Paper
Publication Details
Interim status: Citation only.

Zhang, P., Kumar, K. and Verma, B. (2005). A hybrid classifier for mass classification with different kinds of features in mammography. Paper presented at the 2nd international conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005), Changsha, China.

Access the publisher's website.

2005 HERDC submission.

© Copyright Springer, 2005
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 feature 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.

Citation Information
Ping Zhang, Kuldeep Kumar and Brijesh Verma. "A hybrid classifier for mass classification with different kinds of features in mammography" Paper presented at the 2nd international conference on Fuzzy Systems and Knowledge Discovery (FSKD 2005) (2005)
Available at: http://works.bepress.com/kuldeep_kumar/24/