Paper Info
Title
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.
Year
DOI
Venue
2013
10.1007/978-3-642-41016-1_21
Communications in Computer and Information Science
Keywords
Field
DocType
Alheimer's Disease detection,Mild Cognitive impairment,classification fusion
Clinical tests,Disease,Pattern recognition,Computer science,Image processing,Artificial intelligence,Cognitive impairment
Conference
Volume
ISSN
Citations 
384
1865-0929
2
PageRank 
References 
Authors
0.38
3
10
Name
Order
Citations
PageRank
Christos-Nikolaos Anagnostopoulos1103491.30
Ioannis Giannoukos21739.23
Christian Spenger3603.65
Andrew Simmons420.38
Mecocci Patrizia5995.23
Hilkka Soininen61498.31
Iwona Kloszewska7261.56
Bruno Vellas8342.40
Simon Lovestone9906.51
Magda Tsolaki10608.76