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Proceedings Paper

Feature selection for computer-aided polyp detection using genetic algorithms
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Paper Abstract

To improve computer aided diagnosis (CAD) for CT colonography we designed a hybrid classification scheme that uses a committee of support vector machines (SVMs) combined with a genetic algorithm (GA) for variable selection. The genetic algorithm selects subsets of four features, which are later combined to form a committee, with majority vote for classification across the base classifiers. Cross validation was used to predict the accuracy (sensitivity, specificity, and combined accuracy) of each base classifier SVM. As a comparison for GA, we analyzed a popular approach to feature selection called forward stepwise search (FSS). We conclude that genetic algorithms are effective in comparison to the forward search procedure when used in conjunction with a committee of support vector machine classifiers for the purpose of colonic polyp identification.

Paper Details

Date Published: 2 May 2003
PDF: 9 pages
Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); doi: 10.1117/12.485796
Show Author Affiliations
Meghan T. Miller, National Institutes of Health (United States)
Anna K. Jerebko, National Institutes of Health (United States)
James D. Malley, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 5031:
Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications
Anne V. Clough; Amir A. Amini, Editor(s)

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