Binary classification of brain tumours using a discrete wavelet transform and energy criteria

Carlos Arizmendi, Alfredo Vellido, Enrique Romero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

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.

Original languageEnglish
Title of host publication2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011 - Conference Proceedings
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011 - Bogota, Colombia
Duration: 23 Feb 201125 Feb 2011

Publication series

Name2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011 - Conference Proceedings

Conference

Conference2011 IEEE 2nd Latin American Symposium on Circuits and Systems, LASCAS 2011
Country/TerritoryColombia
CityBogota
Period23/02/1125/02/11

Keywords

  • Bayesian Neural Networks
  • MRS
  • Wavelets

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