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Savio A.,Grupo de Inteligencia Computacional | Grana M.,Grupo de Inteligencia Computacional | Villanua J.,Hospital Donostia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Detection of Alzheimer's disease over brain Magnetic Resonance Imaging (MRI) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from VBM studies of the MRI data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature selection is based on the correlation between the scalar values computed from the deformation maps and the control variable. Results with linear kernel SVM reach accuracies comparable to previous best results. © 2011 Springer-Verlag.

Savio A.,Grupo de Inteligencia Computacional | Garcia-Sebastian M.T.,Grupo de Inteligencia Computacional | Chyzyk D.,Grupo de Inteligencia Computacional | Hernandez C.,Grupo de Inteligencia Computacional | And 6 more authors.
Computers in Biology and Medicine | Year: 2011

Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 (MD1) and Alzheimer disease (AD). The feature extraction process is based on the voxel clusters detected by voxel-based morphometry (VBM) analysis of sMRI upon a set of patient and control subjects. This feature extraction process is specific for each kind of disease and is grounded on the findings obtained by medical experts. The 10-fold cross-validation results of several statistical and neural network based classification algorithms trained and tested on these features show high specificity and moderate sensitivity of the classifiers, suggesting that the approach is better suited for rejecting than for detecting early stages of the diseases. © 2011 Elsevier Ltd.

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