Abstract

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Title:: Deep learning approaches for image-based classification of Alzheimers disease

Author(s):: GONG Piqiang1; 2; YAN Zuojian2; LI Xue1; 2; LIN Dongmei3; CHEN Fuming2; 1. School of Medical Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China 2. Department of Engineering, the 940th Hospital of Joint Logistics Support Force of Chinese Peoples Liberation Army, Lanzhou 730050, China 3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Keywords:: Alzheimers disease deep learning magnetic resonance imaging neuroimaging review

Abstract:: Alzheimers disease (AD) is a progressive, irreversible neurodegenerative disorder characterized by gradual brain cell degeneration, leading to progressive decline in cognitive function and ultimately death. Early identification and intervention are critical to AD diagnosis. In recent years, deep learning has further advanced image-based AD classification methods and facilitated the application of deep models in the early AD diagnosis. To achieve accurate early diagnosis and subsequent classification of AD, researchers have integrated deep learning with magnetic resonance imaging to develop more precise models. By analyzing and synthesizing relevant domestic and international literature, this review introduces commonly used public datasets and evaluation criteria for AD, analyzes the application of magnetic resonance imaging in AD classification and its integration with deep learning methods, and highlights the roles of techniques such as convolutional neural networks, transfer learning, attention mechanisms, and multimodal approaches in AD classification. It also discusses the advantages, limitations, and developmental trends of deep learning in AD classification, aiming to provide new insights for the application of deep learning in AD research.