An automatic classification system into either malignant or benign microcalcification from mammograms is a helpful tool in breast cancer diagnosis. From a set of extracted features, a classifying method using neural networks can provide a probability estimation that can help the radiologist in his diagnosis. With this objective in mind, this paper proposes a feature selection algorithm from a massive number of features based on a statistical distance method in conjunction with a genetic algorithm (GA). The use of a statistical distance as optimality criterion was improved with genetic algorithms for selecting an appropriate subset of features, thus making this algorithm capable of performing feature selection from a massive set of initial features. Additionally, it provides a criterion to select an appropriate number of features to be employed. Experimental work was performed using Generalized Softmax Perceptrons (GSP), trained with a Strict Sense Bayesian cost function for direct probability estimation, as microcalcification classifiers. A Posterior Probability Model Selection (PPMS) algorithm was employed to determine the network complexity. Results showed that this algorithm converges into a subset of features which has a good classification rate and Area Under Curve (AUC) of the Receiver Operating Curve (ROC). {\^A}{\textcopyright} Springer-Verlag Berlin Heidelberg 2007.

}, keywords = {Breast cancer, Diagnosis, Feature extraction, Genetic algorithms, Mammography, Microcalcification classification, Network complexity, Neural network classifiers, Neural networks, Tumors}, isbn = {9783540742715}, issn = {03029743}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149142403\&partnerID=40\&md5=ef139db3a0e5d603c4f721316abdcf2c}, author = {Gonzalo Vegas S{\'a}nchez-Ferrero and J I Arribas} }