Multinetwork classification scheme for detection of colonic polyps in CT colonography data sets

Anna K. Jerebko; James D. Malley; Marek Franaszek; Ronald M. Summers

doi:10.1117/12.463584

24 April 2002 Multinetwork classification scheme for detection of colonic polyps in CT colonography data sets

Anna K. Jerebko, James D. Malley, Marek Franaszek, Ronald M. Summers

Proceedings Volume 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images; (2002) https://doi.org/10.1117/12.463584
Event: Medical Imaging 2002, 2002, San Diego, California, United States

Abstract

A multi-network decision classification scheme for colonic polyp detection is presented. The approach is based on the results of voting over several neural networks using different variable sets of size N which are selected randomly or by an expert from a general variable set of size M. Detection of colonic polyps is complicated by a large variety of polypoid looking shapes (haustral folds, leftover stool) on the colon surface. Using various shape and curvature characteristics, intensity, size measurements and texture features to distinguish real polyps from false positives leads to an intricate classification problem. We used 17 features including region density, Gaussian and average curvature and sphericity, lesion size, colon wall thickness, and their means and standard deviations in the vicinity of the prospective polyp. Selection of the most important parameters to reduce a feature set to acceptable size is a generally unsolved problem. The method suggested in this paper uses a collection of subsets of variables. These sets of variables are weighted by their effectiveness. The effectiveness cost function is calculated on the basis of the training and test sample mis-classification rates obtained by the training neural net with the given variable set. The final decision is based on the majority vote across the networks generated using the variable subsets, and takes into account the weighted votes of all nets. This method reduces the flst positive rate by a factor of 1.7 compared to single net decisions. The overall sensitivity and specificity rates reached are 100% and 95% correspondingly. Best specificity and sensitivity rates were reached using back propagation neural nets with one hidden layer trained with the Levenberg-Marquardt algorithm. Ten-fold cross-validation is used to better estimate the true error rates.

Citation Download Citation

Anna K. Jerebko, James D. Malley, Marek Franaszek, and Ronald M. Summers "Multinetwork classification scheme for detection of colonic polyps in CT colonography data sets", Proc. SPIE 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images, (24 April 2002); https://doi.org/10.1117/12.463584

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