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Proceedings Paper

A comparative study of database reduction methods for case-based computer-aided detection systems: preliminary results
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Paper Abstract

In case-based computer-aided decision systems (CB-CAD) a query case is compared to known examples stored in the systems case base (also called a reference library). These systems offer competitive classification performance and are easy to expand. However, they also require efficient management of the case base. As CB-CAD systems are becoming more popular, the problem of case base optimization has recently attracted interest among CAD researchers. In this paper we present preliminary results of a study comparing several case base reduction techniques. We implemented six techniques previously proposed in machine learning literature and applied it to the classification problem of distinguishing masses and normal tissue in mammographic regions of interest. The results show that the random mutation hill climbing technique offers a drastic reduction of the number of case base examples while providing a significant improvement in classification performance. Random selection allowed for reduction of the case base to 30% without notable decrease in performance. The remaining techniques (i.e., condensed nearest neighbor, reduced nearest neighbor, edited nearest neighbor, and All k-NN) resulted in moderate reduction (to 50-70% of the original size) at the cost of decrease in CB-CAD performance.

Paper Details

Date Published: 27 February 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600F (27 February 2009); doi: 10.1117/12.812442
Show Author Affiliations
Maciej A. Mazurowski, Duke Univ. Medical Ctr. (United States)
Jordan M. Malof, Univ. of Louisville (United States)
Jacek M. Zurada, Univ. of Louisville (United States)
Georgia D. Tourassi, Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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