Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12257
Title: Dementia diagnosis by ensemble deep neural networks using FDG-PET scans
Authors: Yiğit, Altuğ
Baştanlar, Yalın
Işık, Zerrin
Keywords: Alzheimer’s diagnosis
Convolutional neural networks
Ensemble learning
Publisher: Springer
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.
URI: https://doi.org/10.1007/s11760-022-02185-4
https://hdl.handle.net/11147/12257
ISSN: 1863-1703
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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  Until 2025-07-01
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