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Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks
2019
PLoS ONE
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
doi:10.1371/journal.pone.0225759
pmid:31805160
pmcid:PMC6894831
fatcat:any5f2ag7feslneve4jeo6zh4y