The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer’s disease using Alzheimer’s Disease Neuroimaging Initiative database


Autoři: Ilker Ozsahin aff001;  Boran Sekeroglu aff003;  Greta S. P. Mok aff001
Působiště autorů: Biomedical Imaging Laboratory, Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China aff001;  Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Turkey aff002;  Department of Information Systems Engineering, Near East University, Nicosia, Turkey aff003;  Faculty of Health Sciences, University of Macau, Macau SAR, China aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226577

Souhrn

Amyloid beta (Aβ) plaques aggregation is considered as the “start” of the degenerative process that manifests years before the clinical symptoms appear in Alzheimer’s Disease (AD). The aim of this study is to use back propagation neural networks (BPNNs) in 18F-florbetapir PET data for automated classification of four patient groups including AD, late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and significant memory concern (SMC), versus normal control (NC) for early AD detection. Five hundred images for AD, LMCI, EMCI, SMC, and NC, i.e., 100 images for each group, were used from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The results showed that the automated classification of NC/AD produced a high accuracy of 87.9%, while the results for the prodromal stages of the disease were 66.4%, 60.0%, and 52.9% for NC/LCMI, NC/EMCI and NC/SMC, respectively. The proposed method together with the image preparation steps can be used for early AD detection and classification with high accuracy using Aβ PET dataset.

Klíčová slova:

Alzheimer's disease – Artificial neural networks – Biomarkers – Magnetic resonance imaging – Neural networks – Neuroimaging – Neurons – Positron emission tomography


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Článek vyšel v časopise

PLOS One


2019 Číslo 12