Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
Pattern Recognition Letters
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Stabilizing Classifiers for Very Small Sample Sizes
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Effective Detection of the Alzheimer Disease by Means of Coronal NMSE SVM Feature Classification
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
SPECT Image Classification Techniques for Computer Aided Diagnosis of the Alzheimer Disease
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Alzheimer's Diagnosis Using Eigenbrains and Support Vector Machines
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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
Support Vector Machines and Neural Networks for the Alzheimer's Disease Diagnosis Using PCA
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
Functional Brain Image Classification Techniques for Early Alzheimer Disease Diagnosis
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 of SPECT Images Using Clustering Techniques Revisited
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
Null point imaging: a joint acquisition/analysis paradigm for MR classification
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Pattern Recognition Letters
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
GMM based SPECT image classification for the diagnosis of Alzheimer's disease
Applied Soft Computing
Computer aided diagnosis of Alzheimer's disease using component based SVM
Applied Soft Computing
Effective diagnosis of alzheimer's disease by means of association rules
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Early diagnosis of Alzheimer's disease based on Partial Least Squares and Support Vector Machine
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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This work aims at providing a tool to assist the interpretation of SPECT images for the diagnosis of Alzheimer's Disease (AD). Our approach is to test classifiers, which uses the intensity values of the images, without any prior information. Such a classifier is built upon a training set, containing images with two different labels (AD patients and normal subjects). It will then provide a classification for any new unknown image. The main problem to be handled is the small number of available images compared to the large number of features (here the image's voxels): the so-called small sample size problem. We evaluate here the ability of two linear classifiers to correctly label a set of 79 images. Our experiments show promising results. They also show that image classification based on intensity values only is possible and might be used for other applications as well.