@article{(Open Science Index):https://publications.waset.org/pdf/14762,
	  title     = {Massive Lesions Classification using Features based on Morphological Lesion Differences},
	  author    = {U. Bottigli and  D.Cascio and  F. Fauci and  B. Golosio and  R. Magro and  G.L. Masala and  P. Oliva and  G. Raso and  S.Stumbo},
	  country	= {},
	  institution	= {},
	  abstract     = {Purpose of this work is the development of an
automatic classification system which could be useful for radiologists
in the investigation of breast cancer. The software has been designed
in the framework of the MAGIC-5 collaboration.
In the 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 on morphological lesion differences.
Some classifiers as a Feed Forward Neural Network, a K-Nearest
Neighbours and a Support Vector Machine are used to distinguish the
pathological records from the healthy ones.
The results obtained in terms of sensitivity (percentage of
pathological ROIs correctly classified) and specificity (percentage of
non-pathological ROIs correctly classified) will be presented through
the Receive Operating Characteristic curve (ROC). In particular the
best performances are 88% ± 1 of area under ROC curve obtained
with the Feed Forward Neural Network.},
	    journal   = {International Journal of Medical and Health Sciences},
	  volume    = {1},
	  number    = {12},
	  year      = {2007},
	  pages     = {662 - 666},
	  ee        = {https://publications.waset.org/pdf/14762},
	  url   	= {https://publications.waset.org/vol/12},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 12, 2007},
	}