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

Author
González, F.F.; Belanche, Ll.
Type of activity
Presentation of work at congresses
Name of edition
8th Mexican International Conference on Artificial Intelligence
Date of publication
2009
Presentation's date
2009
Book of congress proceedings
2009 Eighth mexican international conference on artificial intelligence, MICAI 2009: 9-13 November 2009, Guanajuato, Guanajuato, Mexico
First page
134
Last page
139
Publisher
IEEE Computer Society Publications
DOI
https://doi.org/10.1109/MICAI.2009.26 Open in new window
Repository
http://hdl.handle.net/2117/18002 Open in new window
Abstract
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...
Citation
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.
Keywords
Classification, Feature selection, Visualization
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

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