Deep learning of volumetric 3D CNN for fMRI in Alzheimer's disease classification

Harshit S. Parmar, Brian Nutter, Rodney Long, Sameer Antani, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Functional magnetic resonance imaging has a potential to provide insight into early detectors or biomarkers for various neurological disorders. With the advent of recent developments in deep learning, it may be possible to extract detailed information from neuroimaging data that is difficult to acquire using traditional techniques. Here we propose one such deep learning approach that makes use of a 3D Convolutional Neural Network to predict the onset of Alzheimer's disease even in a single subject based on resting state fMRI data. This approach extracts both spatial and temporal features from the 4D volume and eliminates the traditional complicated steps of feature extraction. In our experiments, a relatively simple deep learning architecture yields high performance in Alzheimer's disease classification. This illustrates the possibility of using volumetric feature extractors and classifiers as a tool to obtain biomarkers for neurological disorders and another step towards use of clinical fMRI.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsAndrzej Krol, Barjor S. Gimi
PublisherSPIE
ISBN (Electronic)9781510634015
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 18 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11317
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CityHouston
Period02/18/2002/20/20

Keywords

  • Alzheimer's Disease
  • Clinical fMRI
  • Convolutional neural networks
  • Deep learning
  • Neuroimaging

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