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

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

doi:10.1117/12.2549038

28 February 2020 Deep learning of volumetric 3D CNN for fMRI in Alzheimer’s disease classification

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

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113170C (2020) https://doi.org/10.1117/12.2549038
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Harshit S. Parmar, Brian Nutter, Rodney Long, Sameer Antani, and Sunanda Mitra "Deep learning of volumetric 3D CNN for fMRI in Alzheimer’s disease classification", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170C (28 February 2020); https://doi.org/10.1117/12.2549038

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available