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

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 variable subsets selected from a general set. We used 21 features including region density, Gaussian and mean curvature and sphericity, lesion size, colon wall thickness, and their means and standard deviations. The subsets of variables are weighted by their effectiveness calculated on the basis of the training and test sample misclassification rates. The final decision is based on the majority vote across the networks and takes into account the weighted votes of all nets. This method reduces the false 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. Back propagation neural nets trained with the Levenberg-Marquardt algorithm were used. Ten-fold cross-validation is applied to better estimate the true error rates.