重庆邮电大学学报（自然科学版）2024，Vol.36Issue(4)：716-728,13.DOI:10.3979/j.issn.1673-825X.202310300349
注意力密集连接网络的早期AD脑形态学表征与分类
Brain morphological representation and classification of early AD based on attention densely connected network
摘要
Abstract
To address the challenges of timely detection,diagnosis,and intervention in the early stages of Alzheimer's dis-ease(AD),a neurodegenerative disorder,we propose a method for extracting and classifying early AD brain morphological features based on attention densely connected network.The design strategy of using a dense connection network as the back-bone architecture and multi-view 3D image information as the network input is adopted,and an attention mechanism is in-troduced to enable the network to capture brain regions that are important for AD classification.Experimental results show that under the experimental environment of this study,the classification accuracy rates for cognitively normal(CN)vs.mild cognitive impairment(MCI),MCI vs.AD,and CN vs.AD have reached 98.37%,97.63%,and 98.60%respectively,re-presenting an advanced level in the field of AD classification.Moreover,through the analysis of attention maps obtained via the attention mechanism,we can identify the evolution trajectory of AD brain morphology.The transformation from CN to MCI involves abnormal changes in brain morphology in subcortical structures,and the further transformation to AD involves abnormal changes in brain morphology in cortical structures.关键词
阿尔茨海默病/脑形态学/密集连接网络/注意力机制/分类Key words
Alzheimer's disease/brain morphology/densely connected network/attention mechanism/classification分类
信息技术与安全科学引用本文复制引用
康迪,赵敏,程和伟,田银,王伟,李章勇..注意力密集连接网络的早期AD脑形态学表征与分类[J].重庆邮电大学学报（自然科学版）,2024,36(4):716-728,13.基金项目
重庆市留学人员回国创业创新支持计划项目(2007010003947888)The Program of Chongqing Innovation and Entrepreneurship for Returned Overseas Scholars of China(2007010003947888) （2007010003947888）
