title: Diagnosis of brain tumours from magnetic resonance spectroscopy using wavelets and Neural Networks.
abstract: The diagnosis of human brain tumours from noninvasive signal measurements is a sensitive task that requires specialized expertise. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this brief paper, a method that combines data pre-processing using wavelets with classification using Artificial Neural Networks is shown to yield high diagnostic classification accuracy for a broad range of brain tumour pathologies.
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: Juan
authors.initials: J
authors.lastname: Hernandez-Tamames
authors.forename: Enrique
authors.initials: E
authors.lastname: Romero
authors.forename: Alfredo
authors.initials: A
authors.lastname: Vellido
authors.forename: Francisco
authors.initials: F
authors.lastname: Del Pozo
mesh_terms: D019540:Area Under Curve; D001932:Brain Neoplasms / Q000175:diagnosis*; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D013223:Statistics as Topic; D058067:Wavelet Analysis