Abstract
We present the results of a systematic and quantitative comparison of methods from pattern recognition for the analysis of clinical magnetic resonance spectra. The medical question being addressed is the detection of brain tumor. In this application we find regularized linear methods to be superior to more flexible methods such as support vector machines, neural networks or random forests. The best preprocessing method for our spectral data is a smoothing and subsampling approach.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Menze, B.H., Wormit, M., Bachert, P., Lichy, M., Schlemmer, HP., Hamprecht, F.A. (2005). Classification of In Vivo Magnetic Resonance Spectra. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_41
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DOI: https://doi.org/10.1007/3-540-28084-7_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
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