@inproceedings{def93a32e70f4954813befc80a8527b9,
title = "Binary classification of brain tumours using a discrete wavelet transform and energy criteria",
abstract = "The accurate diagnosis of human brain tumours is a sensitive medical task, for which radiology experts often must rely on indirect signal measurements. There is thus a need for developing computer-based decision support tools to assist doctors in their diagnostic task. The experiments in this brief paper address such problem in the form of binary classification, for which the pre-processing of the Magnetic Resonance Spectroscopy (MRS) signal is a most relevant data analysis stage. A combination of the Discrete Wavelet Transform (DWT) for signal decomposition and an energy criterion for signal reconstruction is used to pre-process the MRS data prior to the feature selection and classification with Bayesian Neural Networks.",
keywords = "Bayesian Neural Networks, MRS, Wavelets",
author = "Carlos Arizmendi and Alfredo Vellido and Enrique Romero",
year = "2011",
doi = "10.1109/LASCAS.2011.5750304",
language = "Ingl{\'e}s",
isbn = "9781424494859",
series = "2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011 - Conference Proceedings",
booktitle = "2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011 - Conference Proceedings",
note = "2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011 ; Conference date: 23-02-2011 Through 25-02-2011",
}