无线电工程2024，Vol.54Issue(1)：89-97,9.DOI:10.3969/j.issn.1003-3106.2024.01.012
基于注意力和Transformer的阿尔兹海默症分类
Classification of Alzheimer's Disease Based on Attention and Transformer
摘要
Abstract
Alzheimer's Disease(AD)is a neurodegenerative disease with high prevalence,which seriously affects the life of the elderly.Magnetic Resonance Imaging(MRI)can non-invasively obtain the morphological structure of the brain and reveal the pathological changes of the brain,which is currently the main means of AD diagnosis.Deep learning has powerful feature extraction and modeling capabilities in image processing,and the use of deep learning methods to process MRI for automatic diagnosis of AD has great application value.For three-dimensional brain images,the size and location of lesions are random and correlated,and local detailed features and global long-range dependency information are important.An attention based end-to-end network combining 3D CNN and Transformer is proposed to classify AD patients and normal individuals in response to such issues.Firstly,3D CNN is used to extract deep semantic feature-maps,which are then subjected to multi-scale feature weighted attention encoding and globally modeled by Transformer to obtain classification results.The method is validated on the AD dataset and publicly available 3D medical classification datasets.It is shown that the accuracy,sensitivity,and specificity are improved.The accuracy on the AD classification task reaches 95%,and the attention maps of the model highlight the disease-related areas such as the frontal lobe and the posterior cingulate cortex.The results show that the method has good classification performance and can be used as an automatic,effective,and convenient method for auxiliary diagnosis of AD and other medical tasks.关键词
卷积神经网络/Transformer/磁共振成像/图像分类/阿尔兹海默症Key words
CNN/Transformer/MRI/image classification/AD分类
信息技术与安全科学引用本文复制引用
汪悦恺,王文伟,孟慧茹..基于注意力和Transformer的阿尔兹海默症分类[J].无线电工程,2024,54(1):89-97,9.基金项目
湖北省卫健委联合武汉大学中南医院医学科技平台创新支撑项目(PYXM2020006)Medical Technology Platform Innovation Support Project of Hubei Provincial Health Commission in Collaboration with Wuhan University Central South Hospital(PYXM2020006) （PYXM2020006）
