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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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A Tutorial on Support Vector Machines for Pattern Recognition
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BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
The Diagnosis of Alzheimer's Disease Based on Voxel-Based Morphometry and Support Vector Machine
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 02
Artificial Intelligence in Medicine
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IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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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
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Image and Vision Computing
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IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
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LIBSVM: A library for support vector machines
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MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
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
Cocaine dependent classification using brain magnetic resonance imaging
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Deformation based feature selection for Computer Aided Diagnosis of Alzheimer's Disease
Expert Systems with Applications: An International Journal
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IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
A survey of multiple classifier systems as hybrid systems
Information Fusion
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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.