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

Abstract

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.