Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks


Autoři: Arjun Punjabi aff001;  Adam Martersteck aff002;  Yanran Wang aff001;  Todd B. Parrish aff002;  Aggelos K. Katsaggelos aff001
Působiště autorů: Department of Electrical Engineering and Computer Science/McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America aff001;  Department of Radiology/Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America aff002;  Mesulam Cognitive Neurology and Alzheimer’s Disease Center, Northwestern University, Chicago, Illinois, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225759

Souhrn

Automated methods for Alzheimer’s disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and FDG PET, but a comprehensive and balanced comparison of the MRI and amyloid PET modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer’s dementia classification using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with identical neural network architectures. Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning.

Klíčová slova:

Alzheimer's disease – Longitudinal studies – Magnetic resonance imaging – Neural networks – Neuroimaging – Positron emission tomography – Support vector machines


Zdroje

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PLOS One


2019 Číslo 12