%0 Journal Article
	%A U. Bottigli and  R.Chiarucci and  B. Golosio and  G.L. Masala and  P. Oliva and  S.Stumbo and  D.Cascio and  F. Fauci and  M. Glorioso and  M. Iacomi and  R. Magro and  G. Raso
	%D 2007
	%J International Journal of Medical and Health Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 2, 2007
	%T Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences
	%U https://publications.waset.org/pdf/1568
	%V 2
	%X Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.

	%P 152 - 158