Abstract
We consider the automated diagnosis of Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) in 3D structural MRI brain scans. We develop an efficient deep convolutional neural network (CNN) based classifier by analyzing 3D brain MRI. The proposed model extracts features from the MRI scans and learns significant information related to Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). We perform motion correction, non-uniform intensity normalization, Talairach transform, intensity normalization, and skull-stripping in the raw MRI scans. After that several 2D slices are generated, and center patch is cropped from the slices before passing them to the CNN classifier. Besides, we demonstrate ways to improve the performance of a CNN classifier for AD and MCI diagnosis. We conduct experiments using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset for classification of the AD, MCI and CN (normal/healthy controls) to evaluate the proposed model. The proposed model achieves 94.97% accuracy for AD/CN classification and 91.98% accuracy for AD/MCI classification outperforming baseline models and several competing methods from other studies.
*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Acknowledgement
This study was supported by Brains and Behavior (B&B) Fellowship program from Neuroscience Institute of Georgia State University.
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH–12–2–0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
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Islam, J., Zhang, Y., for the Alzheimer’s Disease Neuroimaging Initiative*. (2018). Deep Convolutional Neural Networks for Automated Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Using 3D Brain MRI. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_34
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