The nature of statistical learning theory
The nature of statistical learning theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Ontology driven decision support for the diagnosis of mild cognitive impairment
Computer Methods and Programs in Biomedicine
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In this paper we present an automated method for diagnosing Alzheimer disease (AD) from brain MR images. The approach uses the scale-invariant feature transforms (SIFT) extracted from different slices in MR images for both healthy subjects and subjects with Alzheimer disease. These features are then clustered in a group of features which they can be used to transform a full 3-dimensional image from a subject to a histogram of these features. A feature selection strategy was used to select those bins from these histograms that contribute most in classifying the two groups. This was done by ranking the features using the Fisher's discriminant ratio and a feature subset selection strategy using the genetic algorithm. These selected bins of the histograms are then used for the classification of healthy/patient subjects from MR images. Support vector machines with different kernels were applied to the data for the discrimination of the two groups, namely healthy subjects and patients diagnosed by AD. The results indicate that the proposed method can be used for diagnose of AD from MR images with the accuracy of %86 for the subjects aged from 60 to 80 years old and with mild AD.