Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns

Farzan A 1 , Mashohor S 2 , Ramli AR 3 , Mahmud R 4

Affiliations 

  • 1 Faculty of Computer Engineering, IAU, Shabestar Branch, Iran. Electronic address: alifarzanam@gmail.com
  • 2 Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia; Institute of Advanced Technology, UPM, Malaysia
  • 3 Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • 4 Faculty of Radiology, University Putra Malaysia (UPM), 43400 Serdang, Selangor D.E., Malaysia
Behav Brain Res, 2015 Sep 1;290:124-30.
PMID: 25889456 DOI: 10.1016/j.bbr.2015.04.010

Abstract

Boosting accuracy in automatically discriminating patients with Alzheimer's disease (AD) and normal controls (NC), based on multidimensional classification of longitudinal whole brain atrophy rates and their intermediate counterparts in analyzing magnetic resonance images (MRI).

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

MeSH terms
Similar publications