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

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 …

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 …

Optimized magnetic resonance imaging (MRI)–based biomarkers of Alzheimer's disease
(AD) may allow earlier detection and refined prediction of the disease. In addition, they could …

Background: In this study, we used a convolutional neural network (CNN) to classify
Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects …

Patches from three orthogonal views of selected cerebral regions can be utilized to learn
convolutional neural network (CNN) models for staging the Alzheimer disease (AD) …

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 …

Several anatomical magnetic resonance imaging (MRI) markers for Alzheimer's disease
(AD) have been identified. Cortical gray matter volume, cortical thickness, and subcortical …

Pattern classification methods have been widely investigated for analysis of brain images to
assist the diagnosis of Alzheimer's disease (AD) and its early stage such as mild cognitive …

Automated structural magnetic resonance imaging (MRI) processing pipelines and different
multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We …