Classification of human brain tumours from MRS data using Discrete Wavelet Transform and Bayesian Neural Networks

Carlos Arizmendi, Alfredo Vellido, Enrique Romero

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is Magnetic Resonance Spectroscopy. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this paper, a Discrete Wavelet Transform procedure was applied to the pre-processing of spectra corresponding to several brain tumour pathologies. This procedure does not alleviate the high dimensionality of the data by itself. For this reason, dimensionality reduction was subsequently implemented using Moving Window with Variance Analysis for feature selection or Principal Component Analysis for feature extraction. The combined method yielded very encouraging results in terms of diagnostic discriminatory binary classification using Bayesian Neural Networks. In most cases, the classification accuracy improved on previously reported results.

Original languageEnglish
Pages (from-to)5223-5232
Number of pages10
JournalExpert Systems with Applications
Volume39
Issue number5
DOIs
StatePublished - Apr 2012
Externally publishedYes

Keywords

  • Bayesian Neural Networks
  • Brain tumours
  • Magnetic Resonance Spectroscopy
  • Medical Decision Support

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