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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.fulltext | With Fulltext | - |
| item.grantfulltext | open | - |
| crisitem.author.dept | Faculty of Computer Science and Engineering | - |
| Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers | |
Files in This Item:
| File | Опис | Size | Format | |
|---|---|---|---|---|
| 10.1.1.404.744.pdf | 126.29 kB | Adobe PDF | View/Open |
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