Machine Learning
Machine Learning
Journal of Cognitive Neuroscience
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Computers in Biology and Medicine
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease
Expert Systems with Applications: An International Journal
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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.