An Ensemble of Classifiers Guided by the AAL Brain Atlas for Alzheimer’s Disease Detection

  • Alexandre Savio
  • Manuel Graña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)


Detection of Alzheimer’s disease based on Magnetic Resonance Imaging (MRI) still is one of the most sought goals in the neuroscientific community. Here, we evaluate a ensemble of classifiers each independently trained with disjoint data extracted from a partition of the brain data volumes performed according to the 116 regions of the Anatomical Automatic Labeling (AAL) brain atlas. Grey-matter probability values from 416 subjects (316 controls and 100 patients) of the OASIS database are estimated, partitioned into AAL regions, and summary statistics per region are computed to create the feature sets. Our objective is to discriminate between control subjects and Alzheimer’s disease patients. For validation we performed a leave-one-out process. Elementary classifiers are linear Support Vector Machines (SVM) with model parameter estimated by grid search. The ensemble is composed of one SVM per AAL region, and we test 6 different methods to make the collective decision. The best performance achieved with this approach is 83.6% accuracy, 91.0% sensitivity, 81.3% specificity and 0.86 of area under the ROC curve. Most discriminant regions for some of the collective decision methods are also provided.


Support Vector Machine Magnetic Resonance Image Data Linear Support Vector Machine FMRIB Software Library Ensemble Decision 
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 2013

Authors and Affiliations

  • Alexandre Savio
    • 1
  • Manuel Graña
    • 1
  1. 1.Grupo de Inteligencia Computacional (GIC)Universidad del País Vasco (UPV/EHU)San SebastiánSpain

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