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LVQ and SVM Classification of FDG-PET Brain Data

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Advances in Self-Organizing Maps and Learning Vector Quantization

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

We apply Generalized Matrix Learning Vector Quantization (GMLVQ) and Support Vector Machine (SVM) classifiers to fluorodeoxyglucose positron emission tomography (FDG-PET) brain data in the hope to achieve better classification accuracies for parkinsonian syndromes as compared to the decision tree method which was used in previous studies. The classifiers are validated using the leave-one-out method. The obtained results show that GMLVQ performs better than the previously studied decision tree (DT) method in the binary classification of group comparisons. Additionally, GMLVQ achieves a superior performance over the DT method regarding multi-class classification. The performance of the considered SVM classifier is comparable with that of GMLVQ. However, in the binary classification, GMLVQ performs better in the separation of Parkinson’s disease subjects from healthy controls. On the other hand, SVM achieves higher accuracy than the GMLVQ method in the binary classification of the other parkinsonian syndromes.

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Author information

Authors and Affiliations

  1. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands

    Deborah Mudali, Michael Biehl & Jos B. T. M. Roerdink

  2. Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Klaus L. Leenders

  3. University of Groningen, University Medical Center Groningen, Neuroimaging Center, Groningen, The Netherlands

    Jos B. T. M. Roerdink

Authors
  1. Deborah Mudali
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  2. Michael Biehl
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  3. Klaus L. Leenders
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  4. Jos B. T. M. Roerdink
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Corresponding authors

Correspondence to Deborah Mudali , Michael Biehl or Jos B. T. M. Roerdink .

Editor information

Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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© 2016 Springer International Publishing Switzerland

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Mudali, D., Biehl, M., Leenders, K.L., Roerdink, J.B.T.M. (2016). LVQ and SVM Classification of FDG-PET Brain Data. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

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