Alzheimer’s Disease Detection Using Minimal Morphometric Features with an Extreme Learning Machine Classifier

  • M. Aswatha Kumar
  • B. S. Mahanand
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


In this paper, we present an accurate method of detection of Alzheimer’s disease using a minimal number of voxel-based morphometry features obtained from the brain MRI scans. The problem of early detection of AD is formulated as a binary classification problem and solved using an extreme learning machine classifier. The functional relationship between the voxel-based morphometry features extracted from magnetic resonance images and Alzheimer’s disease is approximated closely using the extreme learning machine classifier. Since, the extreme learning machine is computationally efficient and provides a better generalization ability, Principal Component Analysis along with the Extreme Learning Machine classifier (referred to here as the PCA-ELM classifier) is used to select the minimal set of morphometric features from the brain MRI images for Alzheimer’s disease detection. Performance of the PCA-ELM classifier is evaluated using the Open Access Series of Imaging Studies (OASIS) data set. The results are also compared with the well-known support vector machine classifier. The study results clearly show that the PCA-ELM classifier produces a better generalization performance with a minimal set of features.


Support Vector Machine Extreme Learn Machine Hide Neuron Support Vector Machine Classifier Clinical Dementia Rate 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. NeuroImage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  2. 2.
    Ashburner, J., Friston, K.J.: Unified segmentation. NeuroImage 26, 839–851 (2005)CrossRefGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar
  4. 4.
    Davatzikos, C., Fan, Y., Wu, X., Shen, D., Resnick, S.M.: Detection of prodromal Alzhei-mer’s disease via pattern classification of MRI. Neurobiology of Aging 29, 514–523 (2008)CrossRefGoogle Scholar
  5. 5.
    El-Dahshan, E.S.A., Hosny, T., Salem, A.B.M.: Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing 20(2), 433–441 (2010)CrossRefGoogle Scholar
  6. 6.
    Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.B., Frith, C.D., Frackowiak, R.S.J.: Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping 2, 189–210 (1994)CrossRefGoogle Scholar
  7. 7.
    Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: A survey. Int. J. Machine Leaning and Cybernetics 2(2), 107–122 (2011)CrossRefGoogle Scholar
  8. 8.
    Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  9. 9.
    Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack Jr., C.R., Ashburner, J., Frackowiak, R.S.J.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3), 681–689 (2008)CrossRefGoogle Scholar
  10. 10.
    Mahanand, B.S., Suresh, S., Sundararajan, N., Aswatha Kumar, M.: Alzheimer’s disease detection using a self-adaptive resource allocation network classifier. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2011), San Jose, USA, pp. 1930–1934 (2011)Google Scholar
  11. 11.
    Mahanand, B.S., Suresh, S., Sundararajan, N., Aswatha Kumar, M.: Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network. Neural Networks (2012), doi:10.1016/j.neunet.2012.02.035Google Scholar
  12. 12.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cognitive Neuroscience 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  13. 13.
    Oja, E.: Neural networks, principal components and subspaces. Int. J. Neural Systems 1, 61–68 (1989)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Savio, A., García-Sebastián, M., Hernández, C., Graña, M., Villanúa, J.: Classification Results of Artificial Neural Networks for Alzheimer’s Disease Detection. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 641–648. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Suresh, S., Dong, K., Kim, H.J.: A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 73(16-18), 3012–3019 (2010)CrossRefGoogle Scholar
  16. 16.
    Suresh, S., Mani, V., Omkar, S.N., Kim, H.J.: Divisible load scheduling in tree network with limited memory: A genetic algorithm and linear programming approach. Int. J. Parallel Emergent and Distributed System 21(5), 303–321 (2006)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Zhang, J., Yan, B., Huang, X., Yang, P., Huang, C.: The diagnosis of Alzheimer’s disease based on voxel-based morphometry and support vector machine. In: Proceedings of Fourth International Conference on Natural Computation (ICNC 2008), Jinan, Shandong province, China, October 18-20, vol. 2, pp. 197–201 (2008)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringM.S. Ramaiah Institute of TechnologyBangaloreIndia
  2. 2.Department of Information Science and EngineeringSri Jayachamarajendra College of EngineeringMysoreIndia

Personalised recommendations