计算机与现代化Issue(7)：15-20,6.DOI:10.3969/j.issn.1006-2475.2025.07.003
基于ConvNeXt和注意力的多动症分类
ADHD Classification Based on ConvNeXt and Attention
摘要
Abstract
Attention Deficit and Hyperactivity Disorder(ADHD),commonly known as ADHD,is a common behavioral disorder in children.Since there is no clear etiology for ADHD,and there are only subtle differences in the brain structure between ADHD patients and normal children,which makes it difficult for clinicians to make effective diagnosis.For such disorders,a convolu-tional neural network based on ConvNeXt and attentional mechanisms is proposed for distinguishing ADHD patients from normal children.Firstly,the sMRI is preprocessed,secondly,the pre-trained model is loaded,the deep feature extraction is performed by the ConvNeXt network containing multidimensional collaborative attention,the ConvNeXt output layer is reconstructed and the final classification results are obtained.Validated on the ADHD-200 dataset,the experimental results show that its classifica-tion accuracy reaches 97.3%,which is better than the current mainstream methods,and the heat map of the model suggests the prefrontal lobe and other brain regions related to the disease,so it can be used as an effective and convenient auxiliary diagnosis method for ADHD.关键词
多动症/ConvNeXt/图像分类/磁共振成像Key words
ADHD/ConvNeXt/image classification/magnetic resonance imaging分类
信息技术与安全科学引用本文复制引用
汪涛,吴茜..基于ConvNeXt和注意力的多动症分类[J].计算机与现代化,2025,(7):15-20,6.基金项目
安徽省高校科学研究项目(2022AH050660) （2022AH050660）
