干旱区生态环境知识资源中心(Arid): Adaptive 3DCNN-Based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease

Arid

DOI	10.1109/TCSS.2022.3223999
	Adaptive 3DCNN-Based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease
	Pan, Dan; Luo, Genqiang; Zeng, An; Zou, Chao; Liang, Haolin; Wang, Jianbin; Zhang, Tong; Yang, Baoyao
通讯作者	Zeng, A
来源期刊	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
ISSN	2329-924X
出版年	2024
卷号	11 期号:1 页码:247-266
英文摘要	An adaptive interpretable ensemble model based on a 3-D convolutional neural network (3DCNN) and genetic algorithm (GA), i.e., 3DCNN+EL+GA, was proposed to differentiate the subjects with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and also identify the discriminative brain regions significantly contributing to the classifications in a data-driven way. The testing results on the datasets from both the AD Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) indicated that 3DCNN+EL+GA outperformed other state-of-the-art deep learning algorithms. More importantly, in these identified brain regions, the discriminative brain subregions at a voxel level were further located with a gradient-based attribution method designed for CNN and illustrated intuitively. Besides these, the behavioral domains corresponding to every identified discriminative brain region (e.g., the rostral hippocampus) were analyzed. It was shown that the resultant behavioral domains were consistent with those brain functions (e.g., emotion) impaired early in the AD process. Further research is needed to examine the generalizability of the proposed ideas and methods in identifying discriminative brain regions and subregions for the diagnosis of other brain disorders (especially little-known ones), such as Parkinson's disease, epilepsy, severe depression, autism, Huntington's disease, multiple sclerosis, and amyotrophic lateral sclerosis, using neuroimaging.
英文关键词	Alzheimer's disease (AD) attribution methods convolutional neural network (CNN) deep learning (DL) ensemble learning (EL) genetic algorithm interpretability magnetic resonance imaging
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000896613800001
WOS关键词	MILD COGNITIVE IMPAIRMENT
WOS类目	Computer Science, Cybernetics ; Computer Science, Information Systems
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404146
推荐引用方式 GB/T 7714	Pan, Dan,Luo, Genqiang,Zeng, An,et al. Adaptive 3DCNN-Based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease[J],2024,11(1):247-266.
APA	Pan, Dan.,Luo, Genqiang.,Zeng, An.,Zou, Chao.,Liang, Haolin.,...&Yang, Baoyao.(2024).Adaptive 3DCNN-Based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,11(1),247-266.
MLA	Pan, Dan,et al."Adaptive 3DCNN-Based Interpretable Ensemble Model for Early Diagnosis of Alzheimer's Disease".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 11.1(2024):247-266.