Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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.
Description
Keywords
Radiaton, Engineering, Artificial intelligence, Tumors, Brain, Support vector machines, Learning algorithms, Regression analysis, Magnetic resonance spectroscopy, Phosphorus, Metabolites, Brain tumors, Classification algorithm, Peak intensity, Human brain tumors, Logistic regressions, Magnetic resonance spectroscopic imaging, Magnetic resonance
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.