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Proceedings Paper

Hybrid committee classifiers for a computerized colonic polyp detection system
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Paper Abstract

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features (numbers varied from 10-20) were selected for 11 NN classifiers which were again combined to form a NN committee classifier. Finally, a hybrid committee classifier was defined by combining the outputs of both the SVM and NN committees. The method was tested on CTC scans (supine and prone views) of 29 patients, in terms of the partial area under a free response receiving operation characteristic (FROC) curve (AUC). Our results showed that the hybrid committee classifier performed the best for the prone scans and was comparable to other classifiers for the supine scans.

Paper Details

Date Published: 15 March 2006
PDF: 9 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445A (15 March 2006);
Show Author Affiliations
Jiang Li, National Institutes of Health (United States)
Jianhua Yao, National Institutes of Health (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)
Ronald M. Summers M.D., National Institutes of Health (United States)
Amy K. Hara, Mayo Clinic Scottsdale (United States)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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