Journal of Cognitive Neuroscience
Results of an Adaboost Approach on Alzheimer's Disease Detection on MRI
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Classification results of artificial neural networks for Alzheimer's disease detection
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Computers in Biology and Medicine
Designing fusers on the basis of discriminants – evolutionary and neural methods of training
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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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.