Computer Aided Diagnosis of CT focal liver lesions by an ensemble of Neural Network and statistical classifiers

Valavanis, I; Mougiakakou, SG; Nikita, KS; Nikita, A

dc.contributor.author	Valavanis, I	en
dc.contributor.author	Mougiakakou, SG	en
dc.contributor.author	Nikita, KS	en
dc.contributor.author	Nikita, A	en
dc.date.accessioned	2014-03-01T02:42:33Z
dc.date.available	2014-03-01T02:42:33Z
dc.date.issued	2004	en
dc.identifier.issn	10987576	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31042
dc.subject	Computed Tomography	en
dc.subject	Computer Aided Diagnosis	en
dc.subject	Ensemble of Classifiers	en
dc.subject	Feature Selection	en
dc.subject	Genetic Algorithm	en
dc.subject	Texture Features	en
dc.subject	Neural Network	en
dc.subject	Region of Interest	en
dc.subject.other	Gray level difference method (GLDM)	en
dc.subject.other	Probabilistic neural network (PNN)	en
dc.subject.other	Regions of interest (ROI)	en
dc.subject.other	Spatial gray level dependence matrix (SGLDM)	en
dc.subject.other	Biological organs	en
dc.subject.other	Computer aided diagnosis	en
dc.subject.other	Computer architecture	en
dc.subject.other	Feature extraction	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Image quality	en
dc.subject.other	Imaging techniques	en
dc.subject.other	Neural networks	en
dc.subject.other	Radiology	en
dc.subject.other	Tissue	en
dc.subject.other	Computerized tomography	en
dc.title	Computer Aided Diagnosis of CT focal liver lesions by an ensemble of Neural Network and statistical classifiers	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IJCNN.2004.1380907	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IJCNN.2004.1380907	en
heal.publicationDate	2004	en
heal.abstract	A Computer Aided Diagnosis (CAD) system for the characterization of hepatic tissue from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) corresponding to four types of hepatic tissue are drawn by an experienced radiologist on abdominal non-enhanced CT images. For each ROI, five sets of texture features are extracted and combined to provide input to the CAD system. If the dimensionality of a feature set is greater than a predefined threshold, appropriate feature selection based on a Genetic Algorithm (GA) is applied. Classification of the ROI is then carried out using an ensemble of classifiers consisting of two Neural Network (NN) and three statistical classifiers. The final decision of the CAD system is based on the application of a voting scheme across the outputs of the primary classifiers of the ensemble. A classification performance of the order of 90.63% was finally achieved.	en
heal.journalName	IEEE International Conference on Neural Networks - Conference Proceedings	en
dc.identifier.doi	10.1109/IJCNN.2004.1380907	en
dc.identifier.volume	3	en
dc.identifier.spage	1929	en
dc.identifier.epage	1934	en