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Using machine learning techniques to explore 1H-MRS data of brain tumors

Autor
González, F.F.; Belanche, Ll.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
8th Mexican International Conference on Artificial Intelligence
Any de l'edició
2009
Data de presentació
2009
Llibre d'actes
2009 Eighth mexican international conference on artificial intelligence, MICAI 2009: 9-13 November 2009, Guanajuato, Guanajuato, Mexico
Pàgina inicial
134
Pàgina final
139
Editor
IEEE Computer Society Publications
DOI
https://doi.org/10.1109/MICAI.2009.26 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/18002 Obrir en finestra nova
Resum
Machine learning is a powerful paradigm to analyze Proton Magnetic Resonance Spectroscopy (1H-MRS) spectral data for the classification of brain tumor pathologies. An important characteristic of this task is the high dimensionality of the involved data sets. In this work we apply filter feature selection methods on three types of 1H-MRS spectral data: long echo time, short echo time and an ad hoc combination of both. The experimental findings show that feature selection permits to drastically re...
Citació
González, F.F.; Belanche, Ll. Using machine learning techniques to explore 1H-MRS data of brain tumors. A: Mexican International Conference on Artificial Intelligence. "2009 Eighth mexican international conference on artificial intelligence, MICAI 2009: 9-13 November 2009, Guanajuato, Guanajuato, Mexico". Guanajuato: IEEE Computer Society Publications, 2009, p. 134-139.
Paraules clau
Classification, Feature selection, Visualization
Grup de recerca
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants

Arxius