Paper Info
Title
Hybrid Committee Classifler for a Computerized Colonic Polyp Detection System
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.
Year
DOI
Venue
2006
10.1117/12.652724
Proceedings of SPIE
Keywords
Field
DocType
computer-aided detection,pattern recognition,statistical methods,classifier committee,neural network,support vector machine
CAD,Search algorithm,Pattern recognition,Support vector machine,Computer-aided diagnosis,Artificial intelligence,Virtual colonoscopy,Engineering,Classifier (linguistics),Artificial neural network,Machine learning,Quadratic classifier
Conference
Volume
ISSN
Citations 
6144
0277-786X
1
PageRank 
References 
Authors
0.36
13
5
Name
Order
Citations
PageRank
Jiang Li141.49
Jianhua Yao21135110.49
Nicholas Petrick320942.63
Ronald M. Summers410.36
Amy K Hara5103.30