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Early Diagnosis of Alzheimer’s Disease by Ensemble Deep Learning Using FDG-PET

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11266)

Abstract

Early diagnosis of Alzheimer’s disease (AD) is critical in preventing from irreversible damages to brain cognitive functions. Most computer-aided approaches consist of extraction of image features to describe the pathological changes and construction of a classifier for dementia identification. Deep learning technique provides a unified framework for simultaneous representation learning and feature classification, and thus avoids the troublesome hand-crafted feature extraction and feature engineering. In this paper, we propose an ensemble of AlexNets (EnAlexNets) algorithm for early diagnosis of AD using positron emission tomography (PET). We first use the automated anatomical labeling (AAL) cortical parcellation map to detect 62 brain anatomical volumes, then extract image patches in each kind of volumes to fine-tune a pre-trained AlexNet, and finally use the ensemble of those well-performed AlexNets as the classifier. We have evaluated this algorithm against seven existing algorithms on an ADNI dataset. Our results indicate that the proposed EnAlexNets algorithm outperforms those seven algorithms in differentiating AD cases from normal controls.

Keywords

  • Alzheimer’s disease
  • Deep learning
  • Computer-aided diagnosis
  • AlexNet

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61471297 and 61771397, in part by the China Postdoctoral Science Foundation under Grant 2017M623245, in part by the Fundamental Research Funds for the Central Universities under Grant 3102018zy031, and in part by the Australian Research Council (ARC) Grants.

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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Zheng, C., Xia, Y., Chen, Y., Yin, X., Zhang, Y. (2018). Early Diagnosis of Alzheimer’s Disease by Ensemble Deep Learning Using FDG-PET. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-02698-1_53

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