Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T
dc.contributor.author | Er, Füsun | |
dc.contributor.author | Hatay, Gökçe Hale | |
dc.contributor.author | Yıldırım, Muhammed | |
dc.contributor.author | Öztürk , Esin Işık | |
dc.contributor.buuauthor | Ökeer, Emre | |
dc.contributor.buuauthor | Hakyemez, Bahattin | |
dc.contributor.department | Uludağ Üniversitesi/Tıp Fakültesi/Radyoloji Anabilim Dalı. | tr_TR |
dc.contributor.orcid | 0000-0002-3425-0740 | tr_TR |
dc.contributor.researcherid | AAI-2318-2021 | tr_TR |
dc.contributor.scopusid | 56529606700 | tr_TR |
dc.contributor.scopusid | 6602527239 | tr_TR |
dc.date.accessioned | 2024-02-13T05:53:44Z | |
dc.date.available | 2024-02-13T05:53:44Z | |
dc.date.issued | 2014 | |
dc.description.abstract | This study aims classification of phosphorus magnetic resonance spectroscopic imaging (P-31-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy P-31 MR spectra acquired at 3T. The spectra were classified based on metabolite characteristics using logistic regression and support vector machine. This study showed that machine learning could be successfully applied for classification of P-31-MR spectra of brain tumors. Future studies will measure the performance of classification algorithms for P-31-MRSI of brain tumors in a larger patient cohort. | en_US |
dc.identifier.citation | Er, F. C.. vd. (2014). "Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T". IEEE Engineering in Medicine and Biology Society Conference Proceedings, 2014 36. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2392-2395. | en_US |
dc.identifier.eissn | 1557-170X | |
dc.identifier.endpage | 2395 | tr_TR |
dc.identifier.isbn | 978-1-4244-7929-0 | |
dc.identifier.pubmed | 25570471 | tr_TR |
dc.identifier.scopus | 2-s2.0-84929484373 | tr_TR |
dc.identifier.startpage | 2392 | tr_TR |
dc.identifier.uri | https://hdl.handle.net/11452/39639 | en_US |
dc.identifier.wos | 000350044702094 | tr_TR |
dc.indexed.wos | CPCIS | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.collaboration | Yurt içi | tr_TR |
dc.relation.collaboration | Yurt dışı | tr_TR |
dc.relation.journal | IEEE Engineering in Medicine and Biology Society Conference Proceedings, 2014 36. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Radiaton | en_US |
dc.subject | Engineering | en_US |
dc.subject | Artificial intelligence | en_US |
dc.subject | Tumors | en_US |
dc.subject | Brain | en_US |
dc.subject | Support vector machines | en_US |
dc.subject | Learning algorithms | en_US |
dc.subject | Regression analysis | en_US |
dc.subject | Magnetic resonance spectroscopy | en_US |
dc.subject | Phosphorus | en_US |
dc.subject | Metabolites | en_US |
dc.subject | Brain tumors | en_US |
dc.subject | Classification algorithm | en_US |
dc.subject | Peak intensity | en_US |
dc.subject | Human brain tumors | en_US |
dc.subject | Logistic regressions | en_US |
dc.subject | Magnetic resonance spectroscopic imaging | en_US |
dc.subject | Magnetic resonance | en_US |
dc.subject.emtree | Adult | en_US |
dc.subject.emtree | Brain neoplasms | en_US |
dc.subject.emtree | Diagnostic use | en_US |
dc.subject.emtree | Female | en_US |
dc.subject.emtree | Human | en_US |
dc.subject.emtree | Middle aged | en_US |
dc.subject.emtree | Nuclear magnetic resonance imaging | en_US |
dc.subject.emtree | Nuclear magnetic resonance spectroscopy | en_US |
dc.subject.emtree | Procedures | en_US |
dc.subject.emtree | Receiver operating characteristic | en_US |
dc.subject.emtree | Statistical model | en_US |
dc.subject.emtree | Support vector machine | en_US |
dc.subject.emtree | Phosphorus | en_US |
dc.subject.mesh | Adult | en_US |
dc.subject.mesh | Brain neoplasms | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Logistic models | en_US |
dc.subject.mesh | Magnetic resonance imaging | en_US |
dc.subject.mesh | Magnetic resonance spectroscopy | en_US |
dc.subject.mesh | Middle aged | en_US |
dc.subject.mesh | Phosphorus | en_US |
dc.subject.mesh | Roc curve | en_US |
dc.subject.mesh | Support vector machines | en_US |
dc.subject.scopus | Cerebral Blood Volume; N Acetylaspartic Acid; Glioma | en_US |
dc.subject.wos | Engineering, biomedical | en_US |
dc.subject.wos | Engineering, electrical & electronic | en_US |
dc.title | Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T | en_US |
dc.type | Article | en_US |
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