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2 May 2003 Feature selection for computer-aided polyp detection using genetic algorithms
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Meghan T. Miller, Anna K. Jerebko, James D. Malley, and Ronald M. Summers "Feature selection for computer-aided polyp detection using genetic algorithms", Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); https://doi.org/10.1117/12.485796
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