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Fuzzy preprocessing of gold standards as applied to a neural network classifier of magnetic resonance spectra

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1 Author(s)
Pizzi, N. ; Dept. of Biodiagnostics, Nat. Res. Council of Canada, Winnipeg, Man., Canada

Culling diagnostic information from biomedical spectra is often exasperated by an imperfect or imprecise gold standard. A fuzzy set theoretic preprocessing method is described that reduces the classification error rate by enhancing a gold standard through the incorporation of nonsubjective within-group centroid information. Magnetic resonance spectra of human brain neoplasms were used to determine the effectiveness of this strategy. A multi-layer perceptron classifier was used as the performance benchmark

Published in:

WESCANEX 97: Communications, Power and Computing. Conference Proceedings., IEEE

Date of Conference:

22-23 May 1997