A two-stage classifier system for normal mammogram identification

Yajie Sun; Charles F. Babbs; Edward J. Delp III

doi:10.1117/12.538996

21 May 2004 A two-stage classifier system for normal mammogram identification

Yajie Sun, Charles F. Babbs, Edward J. Delp III

Proceedings Volume 5299, Computational Imaging II; (2004) https://doi.org/10.1117/12.538996
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

In this paper, we present a unique two-stage classifier system for identifying normal mammograms. We present methods that extract features from breast regions characterizing normal and cancerous tissue. A subset of the features is used to construct a classifier. This classifier is then used to classify each mammogram as normal or abnormal. We designed a unique two-stage cascading classifier system. A binary decision tree classifier was used in the first stage. Cost constraints can be set to correctly classify cancerous regions. The regions classified as abnormal in the first-stage were used as input to the second-stage classifier, a linear classifier. We will show that the overall performance of our two-stage cascading classifier is better than a single classifier. Results of full-field normal mammogram analysis using this cascading classifier are comparable to a human reader.

Citation Download Citation

Yajie Sun, Charles F. Babbs, and Edward J. Delp III "A two-stage classifier system for normal mammogram identification", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); https://doi.org/10.1117/12.538996

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