Dementia diagnosis by ensemble deep neural networks using FDG-PET scans

Altuğ Yiğit, Yalın Baştanlar, Zerrin Işık

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Dementia is a type of brain disease that affects the mental abilities. Various studies utilize PET features or some two-dimensional brain perspectives to diagnose dementia. In this study, we have proposed an ensemble approach, which employs volumetric and axial perspective features for the diagnosis of Alzheimer’s disease and the patients with mild cognitive impairment. We have employed deep learning models and constructed two disparate networks. The first network evaluates volumetric features, and the second network assesses grid-based brain scan features. Decisions of these networks were combined by an adaptive majority voting algorithm to create an ensemble learner. In the evaluations, we compared ensemble networks with single ones as well as feature fusion networks to identify possible improvement; as a result, the ensemble method turned out to be promising for making a diagnostic decision. The proposed ensemble network achieved an average accuracy of 91.83% for the diagnosis of Alzheimer’s disease; to the best of our knowledge, it is the highest diagnosis performance in the literature.
Original languageEnglish
Pages (from-to)2203-2210
JournalSignal, Image and Video Processing
Volume16
Issue number8
DOIs
Publication statusPublished - 1 Nov 2022
Externally publishedYes

Funding

A. Yiğit is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 2211-C Scholarship. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. A. Yiğit is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) 2211-C Scholarship. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

FundersFunder number
DOD ADNI
TUBITAK
National Institutes of HealthU01 AG024904
U.S. Department of DefenseW81XWH-12-2-0012
University of Southern California
Alzheimer's Disease Neuroimaging Initiative
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

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