title: Brain tumour classification using Gaussian decomposition and neural networks.
abstract: The development, implementation and use of computer-based medical decision support systems (MDSS) based on pattern recognition techniques holds the promise of substantially improving the quality of medical practice in diagnostic and prognostic tasks. In this study, the core of a decision support system for brain tumour classification from magnetic resonance spectroscopy (MRS) data is presented. It combines data pre-processing using Gaussian decomposition, dimensionality reduction using moving window with variance analysis, and classification using artificial neural networks (ANN). This combination of techniques is shown to yield high diagnostic classification accuracy in problems concerning diverse brain tumour pathologies, some of which have received little attention in the literature.
authors.affiliation: Department of Computer Languages and Systems at Technical University of Catalonia, Barcelona 08034, Spain. avellido@lsi.upc.edu
authors.forename: Carlos
authors.initials: C
authors.lastname: Arizmendi
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Sierra
authors.forename: Alfredo
authors.initials: A
authors.lastname: Vellido
authors.forename: Enrique
authors.initials: E
authors.lastname: Romero
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis*; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D016571:Neural Networks, Computer; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity