Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12257
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYiğit, Altuğen_US
dc.contributor.authorBaştanlar, Yalınen_US
dc.contributor.authorIşık, Zerrinen_US
dc.date.accessioned2022-08-03T13:09:21Z-
dc.date.available2022-08-03T13:09:21Z-
dc.date.issued2022-
dc.identifier.urihttps://doi.org/10.1007/s11760-022-02185-4-
dc.identifier.urihttps://hdl.handle.net/11147/12257-
dc.description.abstractDementia 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.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAlzheimer’s diagnosisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectEnsemble learningen_US
dc.titleDementia diagnosis by ensemble deep neural networks using FDG-PET scansen_US
dc.typeArticleen_US
dc.authorid0000-0002-3774-6872en_US
dc.institutionauthorBaştanlar, Yalınen_US
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.wosWOS:000770723600001en_US
dc.identifier.scopus2-s2.0-85126816300en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s11760-022-02185-4-
dc.contributor.affiliationDokuz Eylül Üniversitesien_US
dc.contributor.affiliation01. Izmir Institute of Technologyen_US
dc.contributor.affiliationDokuz Eylül Üniversitesien_US
dc.relation.issn1863-1703en_US
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20250701-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.fulltextWith Fulltext-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
Yiğit2022_Article.pdf
  Until 2025-07-01
Article826.2 kBAdobe PDFView/Open    Request a copy
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

4
checked on Jun 21, 2024

WEB OF SCIENCETM
Citations

2
checked on Mar 23, 2024

Page view(s)

19,134
checked on Jun 17, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.