An RBF based classifier for the detection of microcalcifications in mammograms with outlier rejection capability

The results of a study carried out on a large database of mammographic images using an RBF network for density estimation are presented. The classifier is built via the Bayes rule from an estimate of the class conditional probability density functions. The aim is the detection of microcalcifications. Though the recognition rate must be high, a minimum number of false alarms should also be attained. The results obtained using a MLP neural network, K-NN and Gaussian classifiers are also presented for comparison. The receiver operating characteristics curve for image identification demonstrates a superior performance for the RBF classifier where less than 15% of normal images were misclassified for 100% abnormal images identification. A simple outlier detection mechanism has also been examined, which has shown to be useful in flagging data acquisition errors or ambiguous cases also requiring medical attention