Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Functional MRI analysis by a novel spatiotemporal ICA algorithm
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Computer aided diagnosis of Alzheimer's disease using component based SVM
Applied Soft Computing
Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Functional activity maps based on significance measures and Independent Component Analysis
Computer Methods and Programs in Biomedicine
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease
Pattern Recognition Letters
Computer-aided diagnosis system: A Bayesian hybrid classification method
Computer Methods and Programs in Biomedicine
Hi-index | 0.11 |
Finding sensitive and appropriate technologies for early detection of the Alzheimer's disease (AD) are of fundamental importance to develop early treatments. Single Photon Emission Computed Tomography (SPECT) images are non-invasive observation tools to assist the diagnosis, commonly processed through unsupervised statistical tests, or assessed visually. In this work, we present a computer aided diagnosis system based on supervised learning methods, exploring two different novel approaches. Independent Component Analysis (ICA) was used within this work to extract the relevant features from the image database and reduce the feature space dimensionality, to build a SVM with the resulting data. The proposed approach led to an error estimation below the 9%, and was able to detect the AD perfusion pattern and classify new subjects in an unsupervised manner.