Abstract
Computational intelligence techniques were applied to human brain cancer magnetic resonance spectral data. In particular, two approaches, Rough Sets and a Genetic Programming-based Neural Network were investigated and then confirmed via a systematic Individual Dichotomization algorithm. Good preliminary results were obtained with 100% training and 100% testing accuracy that differentiate normal versus malignant samples.
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Barton, A.J., Valdes, J.J. (2008). Computational Intelligence Techniques Applied to Magnetic Resonance Spectroscopy Data of Human Brain Cancers. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_50
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DOI: https://doi.org/10.1007/978-3-540-88425-5_50
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