A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

Journal: Journal of neuroscience methods
Published Date: Aug 20, 2024

Abstract

BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures.

Authors

  • Edvard O S Grødem
    Computational Radiology & Artificial Intelligence unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0372, Oslo, Norway; Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Oslo, Norway. Electronic address: edvardgr@uio.no.
  • Esten Leonardsen
    Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0373, Oslo, Norway.
  • Bradley J MacIntosh
    Division of Radiology and Nuclear Medicine, Computational Radiology & Artificial Intelligence (CRAI), Oslo University Hospital, Oslo, Norway.
  • Atle Bjornerud
  • Till Schellhorn
    Computational Radiology & Artificial Intelligence unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0372, Oslo, Norway.
  • Øystein Sørensen
    Department of Psychology, University of Oslo, Oslo, Norway.
  • Inge Amlien
    Lifespan Changes in Bain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway.
  • Anders M Fjell
    Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Oslo, Norway.

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