Background: Alzheimerâ??s Disease (AD) is a degenerative brain disorder that often\noccurs in people over 65 years old. As advanced AD is difficult to manage, accurate\ndiagnosis of the disorder is critical. Previous studies have revealed effective deep learning\nmethods of classification. However, deep learning methods require a large number\nof image datasets. Moreover, medical images are affected by various environmental\nfactors. In the current study, we propose a deep learning-based method for diagnosis\nof Alzheimerâ??s disease (AD) that is less sensitive to different datasets for external validation,\nbased upon F-18 fluorodeoxyglucose positron emission tomography/computed\ntomography (FDG-PET/CT).
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