A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Data Mining Techniques for the Life Sciences
Data Mining Techniques for the Life Sciences
GMM based SPECT image classification for the diagnosis of Alzheimer's disease
Applied Soft Computing
Lattice independent component analysis for functional magnetic resonance imaging
Information Sciences: an International Journal
Functional brain image classification using association rules defined over discriminant regions
Pattern Recognition Letters
Topology-Based Hierarchical Clustering of Self-Organizing Maps
IEEE Transactions on Neural Networks
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This paper presents a novel computer-aided diagnosis (CAD) tool for the diagnosis of the Alzheimer's disease (AD) using structural Magnetic Resonance Images (MRIs). The proposed method uses information learnt from the tissue distribution of Gray Matter (GM) and White Matter (WM) in the brain, which is previously obtained by an unsupervised segmentation method. The tissue distribution of control (normal) and AD images is modelled by means of Learning Vector Quantization (LVQ) algorithm, generating a set of representative prototypes of each class. The devised method projects new images onto the model vectors space for further classification using Support Vector Machine (SVM). The tool proposed here yields classification results over 90% (accuracy) for controls (normal) and Alzheimer's disease (AD) patients and sensitivity up to 95% to AD. Moreover, statistical significance tests have been also performed in order to validate the proposed approach.