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dc.contributor.authorKazemi, Yosra
dc.contributor.authorHoughten, Sheridan
dc.date.accessioned2023-01-05T14:30:52Z
dc.date.available2023-01-05T14:30:52Z
dc.date.issued2018-5
dc.identifier.urihttp://hdl.handle.net/10464/17138
dc.description.abstractAlzheimer's disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this paper, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer's using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer's disease (AD). The model was implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canadaen_US
dc.publisherIEEE
dc.source2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
dc.subjectAlzheimer's diseaseen_US
dc.subjectDeep learningen_US
dc.titleA deep learning pipeline to classify different stages of Alzheimer's disease from fMRI dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/cibcb.2018.8404980
refterms.dateFOA2023-01-05T14:30:53Z


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