Deep learning approaches for image-based classification of Alzheimer's disease
10.3969/j.issn.1005-202X.2025.11.004
- VernacularTitle:深度学习的阿尔兹海默症影像分类方法
- Author:
Piqiang GONG
1
;
Zuojian YAN
;
Xue LI
;
Dongmei LIN
;
Fuming CHEN
Author Information
1. 甘肃中医药大学医学信息工程学院,甘肃 兰州 730000;中国人民解放军联勤保障部队第940医院医学工程科,甘肃 兰州 730050
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
deep learning;
magnetic resonance imaging;
neuroimaging;
review
- From:
Chinese Journal of Medical Physics
2025;42(11):1420-1433
- CountryChina
- Language:Chinese
-
Abstract:
Alzheimer's 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.
