Brain morphological representation and classification of early AD based on attention densely connected network
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.
国家科技期刊平台
NSTL
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