Classification of magnetic resonance images

Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/23144

DC Field	Value	Language
dc.contributor.author	Trojacanec, Katarina	en_US
dc.contributor.author	Madjarov, Gjorgji	en_US
dc.contributor.author	Gjorgjevikj, Dejan	en_US
dc.contributor.author	Loshkovska, Suzana	en_US
dc.date.accessioned	2022-09-28T07:45:04Z	-
dc.date.available	2022-09-28T07:45:04Z	-
dc.date.issued	2010-06-21	-
dc.identifier.uri	http://hdl.handle.net/20.500.12188/23144	-
dc.description.abstract	The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the images. The dataset used for classification contains magnetic resonance images classified in 9 classes.	en_US
dc.publisher	IEEE	en_US
dc.subject	Classification, Support Vector Machines (SVMs), Magnetic Resonance Images (MRIs), neural networks	en_US
dc.title	Classification of magnetic resonance images	en_US
dc.type	Proceedings	en_US
dc.relation.conference	Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces	en_US
item.grantfulltext	open	-
item.fulltext	With Fulltext	-
crisitem.author.dept	Faculty of Computer Science and Engineering	-
Appears in Collections:	Faculty of Computer Science and Engineering: Conference papers

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