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

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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.

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Notes

  1. http://adni.loni.usc.edu

  2. http://www.SOCR.umich.edu

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Acknowledgements

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.

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Authors and Affiliations

  1. The Graduate School of Natural and Applied Sciences, Dokuz Eylul University, 35390, Buca,Izmir, Turkey

    Altuğ Yiğit

  2. Department of Computer Engineering, Izmir Institute of Technology, 35430, Urla,Izmir, Turkey

    Altuğ Yiğit & Yalın Baştanlar

  3. Department of Computer Engineering, Dokuz Eylul University, 35390, Buca,Izmir, Turkey

    Zerrin Işık

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Yiğit, A., Baştanlar, Y. & Işık, Z. Dementia diagnosis by ensemble deep neural networks using FDG-PET scans. SIViP 16, 2203–2210 (2022). https://doi.org/10.1007/s11760-022-02185-4

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  • DOI: https://doi.org/10.1007/s11760-022-02185-4

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