Hybrid committee classifier for a computerized colonic polyp detection system

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.

[1]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  M.T. Manry,et al.  A modified hidden weight optimization algorithm for feedforward neural networks , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[3]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[4]  N. Japkowicz.  Learning from Imbalanced Data Sets: A Comparison of Various Strategies * , 2000 .

[5]  M.T. Manry,et al.  Optimal pruning of feedforward neural networks based upon the Schmidt procedure , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[6]  Robert M. Nishikawa,et al.  A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[7]  Eric A. Wan,et al.  Neural network classification: a Bayesian interpretation , 1990, IEEE Trans. Neural Networks.

[8]  Ronald M. Summers,et al.  Optimizing the support vector machines (SVM) committee configuration in a colonic polyp CAD system , 2005, SPIE Medical Imaging.

[9]  Kagan Tumer,et al.  Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..

[10]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[11]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[14]  R. M. Summers,et al.  Challenges for computer-aided diagnosis for CT colonography , 2002, Abdominal Imaging.

[15]  Kagan Tumer,et al.  Analysis of decision boundaries in linearly combined neural classifiers , 1996, Pattern Recognit..

[16]  Marek Franaszek,et al.  Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets. , 2003, Academic radiology.

[17]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[18]  Michael T. Manry,et al.  Iterative Design of Neural Network Classifiers Through Regression , 2005, Int. J. Artif. Intell. Tools.

[19]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

[20]  Tang-Kai Yin,et al.  A computer-aided diagnosis for locating abnormalities in bone scintigraphy by a fuzzy system with a three-step minimization approach , 2004, IEEE Transactions on Medical Imaging.

[21]  J. Malley,et al.  Colonic polyps: complementary role of computer-aided detection in CT colonography. , 2002, Radiology.

[22]  Michael T. Manry,et al.  Iterative Improvement of Neural Classifiers , 2004, FLAIRS.

[23]  Hee Chan Kim,et al.  Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features , 2004, IEEE Transactions on Medical Imaging.

[24]  Ronald M. Summers,et al.  Computer aided polyp detection in CT colonography using an ensemble of support vector machines , 2003, CARS.

[25]  Ronald M. Summers,et al.  An Efficient Feature Selection Algorithm for Computer-aided Polyp Detection , 2006, Int. J. Artif. Intell. Tools.

[26]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[27]  Ronald M. Summers,et al.  3D colonic polyp segmentation using dynamic deformable surfaces , 2004, SPIE Medical Imaging.