The use of LS-SVM in the classification of brain tumors based on 1H-MR spectroscopy signals

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The use of LS-SVM in the classification of brain tumors based on 1H-MR spectroscopy signals

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Least Squares Support Vector Machines (LS-SVM) have been developed and successfully applied to classification problems in many areas. In comparison with several other classical methods this technique consistently performs very well on a large variety of problems. Here, results on the application of LS-SVM for classification of brain tumors based on 1H-Magnetic Resonance Spectroscopy (1H-MRS) signals are presented. Radial Basis Function (RBF) and linear kernels are used and compared to find the optimal classifier. The improvement of this classification based on MRS signals will lead to an advanced tool for the discrimination of brain tumors, which is presently under development for the INTERPRET project. (5 pages)

Inspec keywords: medical signal processing; biomedical NMR; tumours; brain; NMR spectroscopy; learning automata

Subjects: Patient diagnostic methods and instrumentation; Medical magnetic resonance imaging and spectroscopy; Biophysics of neurophysiological processes; Digital signal processing; Biomedical magnetic resonance imaging and spectroscopy; Biology and medical computing; Signal processing and detection

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