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Título:
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Feature and model selection in 1H-MRS single voxel spectra for cancer classification
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Autor/a:
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González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract:
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Machine learning is a powerful paradigm within which to analyze 1HMRS spectral data for the classification of tumour pathologies. An important characteristic
of this task is the high dimensionality of the involved data sets. In this work we apply specific feature selection methods in order to reduce the complexity of the problem on two types of 1H-MRS spectral data: long-echo and short-echo time, which present considerable differences in the spectrum for the same cases. The experimental findings show that the feature selection methods enhance the classification
performance of the models induced by several off-the-shelf classifiers and are able to offer very attractive solutions both in terms of prediction accuracy and number of involved spectral frequencies. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica -Machine learning -Proton magnetic resonance spectroscopy -Tumors -- Classification -Aprenentatge automàtic -Tumors -- Classificació |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Capítulo o parte de libro |
Editor:
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Future Technology Press
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