Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets.
OBJECTIVE
A new classification system for colonic polyp detection, designed to increase sensitivity and reduce the number of false-positive findings with computed tomographic colonography, was developed and tested in this study.


METHODS
The system involves classification by a committee of neural networks (NNs), each using largely distinct subsets of features selected from a general set. Back-propagation NNs trained with the Levenberg-Marquardt algorithm were used as primary classifiers (committee members). The set of features included region density, Gaussian and mean curvature and sphericity, lesion size, colon wall thickness, and the means and standard deviations of all of these values. Subsets of variables were initially selected because of their effectiveness according to training and test sample misclassification rates. The final decision for each case is based on the majority vote across the networks and reflects the weighted votes of all networks. The authors also introduce a smoothed cross-validation method designed to improve estimation of the true misclassification rates by reducing bias and variance.


RESULTS
This committee method reduced the false-positive rate by 36%, a clinically meaningful reduction, and improved sensitivity by an average of 6.9% compared with decisions made by any single NN. The overall sensitivity and specificity were 82.9% and 95.3%, respectively, when sensitivity was estimated by means of smoothed cross-validation.


CONCLUSIONS
The proposed method of using multiple classifiers and majority voting is recommended for classification tasks with large sets of input features, particularly when selected feature subsets may not be equally effective and do not provide satisfactory true- and false-positive rates. This approach reduces variance in estimates of misclassification rates.
Jerebko
Anna K
AK
Department of Diagnostic Radiology, Warren Grant Magnuson Clinical Center, National Institutes of Health, Bldg 10, Rm 1C660, 10 Center Dr, MSC 1182, Bethesda, M D 20892-1182, USA.
Malley
James D
JD
Franaszek
Marek
M
Summers
Ronald M
RM
D000465:Algorithms; D003111:Colonic Polyps / Q000145:classification / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity