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Proceedings Paper

A two-stage classifier system for normal mammogram identification
Author(s): Yajie Sun; Charles F. Babbs; Edward J. Delp III
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Paper 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.

Paper Details

Date Published: 21 May 2004
PDF: 11 pages
Proc. SPIE 5299, Computational Imaging II, (21 May 2004); doi: 10.1117/12.538996
Show Author Affiliations
Yajie Sun, Purdue Univ. (United States)
Charles F. Babbs, Purdue Univ. (United States)
Edward J. Delp III, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 5299:
Computational Imaging II
Charles A. Bouman; Eric L. Miller, Editor(s)

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