A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature

Objective: This study investigates the use of automated pattern recognition methods on magnetic resonance data with the ultimate goal to assist clinicians in the diagnosis of brain tumours. Recently, the combined use of magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI) has demonstrated to improve the accuracy of classifiers. In this paper we extend previous work that only uses binary classifiers to assess the type and grade of a tumour to a multiclass classification system obtaining class probabilities. The important problem of input feature selection is also addressed. Methods and Material: Least squares support vector machines (LS-SVMs) with radial basis function kernel are applied and compared with linear discriminant analysis (LDA). Both a Bayesian framework and cross-validation are used to infer the parameters of the LS-SVM classifiers. Four different techniques to obtain multiclass probabilities as a measure of accuracy are compared. Four ...

• Type: Journal
• Year: 2007
• Where: ARTMED
• Authors: Jan Luts, Arend Heerschap, Johan A. K. Suykens, Sabine Van Huffel
• Keywords: ARTMED 2007, Brain Tumours, Class Probabilities, Magnetic Resonance