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

Journal: Behavioural brain research

Published Date: Apr 15, 2015

Abstract

OBJECTIVE: 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).

Authors

Ali Farzan

Faculty of Computer Engineering, IAU, Shabestar Branch, Iran. Electronic address: alifarzanam@gmail.com.
Syansiah Mashohor

Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia; Institute of Advanced Technology, UPM, Malaysia.
Abd Rahman Ramli

Department of Computer & Communication Systems, Faculty of Engineering, University of Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
Rozi Mahmud

Faculty of Radiology, University Putra Malaysia (UPM), 43400 Serdang, Selangor D.E., Malaysia.

Keywords

Aged Alzheimer Disease Atrophy Brain Cluster Analysis Female Follow-Up Studies Humans Magnetic Resonance Imaging Male Principal Component Analysis Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (25889456)

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

Abstract

Authors

Keywords

External Resources

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