Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
412973 | Neurocomputing | 2009 | 13 Pages |
Abstract
Non-invasive techniques such as magnetic resonance spectroscopy (MRS) are often required for assisting the diagnosis of tumours. Radiologists are not always accustomed to make sense of the biochemical information provided by MRS and they may benefit from computer-based support in their decision making. The high dimensionality of the MR spectra obscures atypical aspects of the data that may jeopardize their classification. In this study, we describe a method to overcome this problem that combines nonlinear dimensionality reduction, outlier detection, and expert opinion. MR spectra subsequently undergo a feature selection process followed by classification. The impact of outlier removal on classification performance is assessed.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Alfredo Vellido, Enrique Romero, Félix F. González-Navarro, Lluís A. Belanche-Muñoz, Margarida Julià-Sapé, Carles Arús,