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A comparison of neural network and Bayes recognition approaches to the diagnostic of multiple sclerosis

Content Provider	IEEE Xplore Digital Library
Author	Nehmadi, Y. Guterman, H.
Copyright Year	1995
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel (Nehmadi, Y.; Guterman, H.)
Abstract	This article describes the application of a Multi-Layer Perceptron (MLP) to the problem of diagnosing Multiple Sclerosis (MS). The classification information is obtained by a Trigeminal Evoked Potential (TEP) test. The performance of the MLP is compared with that of the human experts and the Bayes classifier. The efficiency of the neural network and the classical classifiers in conjunction with 4 types of features - the Fourier transform (FT), the peak position, the ARX model coefficient and the temporal wave form - are examined. Although a large clinical data base would be necessary, before this approach can be fully validated, the initial results are very promising. The MLP was found to be less susceptible to the number of features used. The ability of the MLP classifier to generalize is far better than that of the Bayes classifier.
File Size	423959
File Format	PDF
ISBN	0780324986
DOI	10.1109/EEIS.1995.513773
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-03-07
Publisher Place	Israel
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Multiple sclerosis Neural pathways Humans Magnetic resonance imaging Electroencephalography Biological neural networks Computed tomography Signal analysis Decision making
Content Type	Text
Resource Type	Article

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