Article detail · 2014 · conference-paper
Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T
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- YÖKSİSYÖKSİS article record
- YÖKSİS venueConference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
- OpenAlexOpenAlex enrichment (abstract, citations, topics)
Abstract
This study aims classification of phosphorus magnetic resonance spectroscopic imaging ((31)P-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy (31)P 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 (31)P-MR spectra of brain tumors. Future studies will measure the performance of classification algorithms for (31)P-MRSI of brain tumors in a larger patient cohort.
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Citations
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3citationsOpenAlex · cited_by_count (cache / database)
2 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).