In this work we use a large scale regularization approach based on penalized logistic
regression to automatically classify structural MRI images (sMRI) according to cognitive …

In recent years, several high-dimensional, accurate, and effective classification methods
have been proposed for the automatic discrimination of the subject between Alzheimer's …

Background Machine learning neuroimaging researchers have often relied on regularization
techniques when classifying MRI images. Although these were originally introduced to deal …

In this study, we investigate multi-scale features extracted from baseline structural magnetic
resonance imaging (MRI) for classifying patients with mild cognitive impairment (MCI), who …

This paper presents a framework to introduce spatial and anatomical priors in SVM for brain
image analysis based on regularization operators. A notion of proximity based on prior …

Recently, several high dimensional classification methods have been proposed to
automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive …

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more
emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess …

Incorporating with machine learning technology, neuroimaging markers which extracted
from structural Magnetic Resonance Images (sMRI), can help distinguish Alzheimer's …

Purpose We present and evaluate a new automated method based on support vector
machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to …

In this paper, we propose a fully automated method to individually classify patients with
Alzheimer's disease (AD) and elderly control subjects based on anatomical magnetic …