Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural network design
Self-Organizing Maps and Learning Vector Quantization forFeature Sequences
Neural Processing Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Journal of Cognitive Neuroscience
Computers in Biology and Medicine
Deformation based features for alzheimer's disease detection with linear SVM
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Learning parsimonious dendritic classifiers
Neurocomputing
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Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goal in the Neurosciences. We used four different models of Artificial Neural Networks (ANN): Back-propagation (BP), Radial Basis Networks (RBF), Learning Vector Quantization Networks (LVQ) and Probabilistic Neural Networks (PNN) to perform classification of patients of mild Alzheimer's disease vs. control subjects. Features are extracted from the brain volume data using Voxel-based Morphometry (VBM) detection clusters. The voxel location detection clusters given by the VBM were applied to select the voxel values upon which the classification features were computed. We have evaluated feature vectors computed from the GM segmentation volumes using the VBM clusters as voxel selection masks. The study has been performed on MRI volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies (OASIS) database, which is a large number of subjects compared to current reported studies.