Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors
Document typeConference report
Rights accessOpen Access
The combination of an Artificial Neural Network classifier, a feature selection process, and a novel linear dimensionality reduction technique that provides a data projection for visualization and which preserves completely the class discrimination achieved by the classifier, is applied in this study to the analysis of an international, multi-centre 1H-MRS database of brain tumors. This combination yields results that are both intuitively interpretable and very accurate. The method as a whole remains simple enough as to allow its easy integration in existing medical decision support systems.
CitationLisboa, P. [et al.]. Classification, dimensionality reduction, and maximally discriminatory visualization of a multicentre 1H-MRS database of brain tumors. A: IEEE International Conference on Machine Learning and Applications. "7th IEEE International Conference on Machine Learning and Applications". San Diego, California: IEEE, 2008, p. 613-618.
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