Classifier Combination for In Vivo Magnetic Resonance Spectra of Brain Tumours
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Artificial Intelligence in Medicine
The evidence framework applied to classification networks
Neural Computation
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Brain tumor classification based on long echo proton MRS signals
Artificial Intelligence in Medicine
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is Magnetic Resonance Spectroscopy. In this task, radiology experts are likely to benefit from the support of computer-based systems built around robust classification processes. In this paper, a Discrete Wavelet Transform procedure was applied to the pre-processing of spectra corresponding to several brain tumour pathologies. This procedure does not alleviate the high dimensionality of the data by itself. For this reason, dimensionality reduction was subsequently implemented using Moving Window with Variance Analysis for feature selection or Principal Component Analysis for feature extraction. The combined method yielded very encouraging results in terms of diagnostic discriminatory binary classification using Bayesian Neural Networks. In most cases, the classification accuracy improved on previously reported results.