Classification models for Alzheimer'S Disease detection

Abstract

This paper presents a classification fusion for Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) classification based on dataset acquired basically from an automated structural MRI image processing pipeline. The dataset includes eighty-one regional cortical volume and cortical thickness features produced by the automated pipeline, along with two demographic measurements and three manual volume measurements of the hippocampus. This high-dimensional pattern classification problem is tested in a large database that contains clinical tests from six medical centers in Europe. The assessment of the results has shown that with a careful selection of combined classifiers, subject classification in three classes (Normal Controls, patients with MCI or with AD) is fairly accurate and can be used as an assistive tool to clinical examinations. © Springer-Verlag Berlin Heidelberg 2013.