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Support Vector Machines and Neural Networks for the Alzheimer’s Disease Diagnosis Using PCA

  • M. López
  • J. Ramírez
  • J. M. Górriz
  • I. Álvarez
  • D. Salas-Gonzalez
  • F. Segovia
  • M. Gómez-Río
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

In the Alzheimer’s Disease (AD) diagnosis process, functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians. However, the current evaluation of these images entails a succession of manual reorientations and visual interpretation steps, which attach in some way subjectivity to the diagnostic. In this work, two pattern recognition methods have been applied to SPECT and PET images in order to obtain an objective classifier which is able to determine whether the patient suffers from AD or not. A common feature selection stage is first described, where Principal Component Analysis (PCA) is applied over the data to drastically reduce the dimension of the feature space, followed by the study of neural networks and support vector machines (SVM) classifiers. The achieved accuracy results reach 98.33% and 93.41% for PET and SPECT respectively, which means a significant improvement over the results obtained by the classical Voxels-As-Features (VAF) reference approach.

Keywords

Positron Emission Tomography Support Vector Machine Single Photon Emission Compute Tomography Hide Layer Positron Emission Tomography Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Stoeckel, J., Malandain, G., Migneco, O., Koulibaly, P.M., Robert, P., Ayache, N., Darcourt, J.: Classification of SPECT images of normal subjects versus images of alzheimer’s disease patients. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 666–674. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Górriz, J.M., Ramírez, J., Lassl, A., Salas-Gonzalez, D., Lang, E.W., Puntonet, C.G., Álvarez, I., López, M., Gómez-Río, M.: Automatic computer aided diagnosis tool using component-based svm. In: 2008 IEEE Nuclear Science Symposium Conference Record, pp. 4392–4395 (2008)Google Scholar
  3. 3.
    Friston, K.J., Ashburner, J., Kiebel, S.J., Nichols, T.E., Penny, W.D.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, London (2007)CrossRefGoogle Scholar
  4. 4.
    Salas-González, D., Górriz, J.M., Ramírez, J., Lassl, A., Puntonet, C.G.: Improved gauss-newton optimization methods in affine registration of SPECT brain images. IET Electronics Letters 44(22), 1291–1292 (2008)CrossRefGoogle Scholar
  5. 5.
    Turk, M., Petland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 13(1), 71–86 (1991)CrossRefGoogle Scholar
  6. 6.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  7. 7.
    McCulloch, W.S., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bull. Mathematical Biophysics 5, 115–133 (1943)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. López
    • 1
  • J. Ramírez
    • 1
  • J. M. Górriz
    • 1
  • I. Álvarez
    • 1
  • D. Salas-Gonzalez
    • 1
  • F. Segovia
    • 1
  • M. Gómez-Río
    • 2
  1. 1.Dept. of Signal Theory, Networking and CommunicationsUniversity of GranadaSpain
  2. 2.Department of Nuclear MedicineHospital Universitario Virgen de las NievesGranadaSpain

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