Classification Results of Artificial Neural Networks for Alzheimer’s Disease Detection

  • Alexandre Savio
  • Maite García-Sebastián
  • Carmen Hernández
  • Manuel Graña
  • Jorge Villanúa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


Detection of Alzheimer’s disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goal in the Neurosciences. We used four different models of Artificial Neural Networks (ANN): Backpropagation (BP), Radial Basis Networks (RBF), Learning Vector Quantization Networks (LVQ) and Probabilistic Neural Networks (PNN) to perform classification of patients of mild Alzheimer’s disease vs. control subjects. Features are extracted from the brain volume data using Voxel-based Morphometry (VBM) detection clusters. The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classification features were computed. We have evaluated feature vectors computed from the GM segmentation volumes using the VBM clusters as voxel selection masks. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies.


Radial Basis Function Network Probabilistic Neural Network Learn Vector Quantization Feature Extraction Process Accuracy Sensitivity 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Maite García-Sebastián
    • 1
  • Carmen Hernández
    • 1
  • Manuel Graña
    • 1
  • Jorge Villanúa
    • 2
  1. 1.Grupo de Inteligencia ComputacionalSpain
  2. 2.OsatekSan SebastiánSpain

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