An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer's Disease Levels

doi:10.3389/fnagi.2022.930584

OPT OpenIR > 光谱成像技术研究室

	An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer's Disease Levels
	Li, Chao; Wang, Quan; Liu, Xuebin; Hu, Bingliang
作者部门	光谱成像技术研究室
	2022-07-11
发表期刊	FRONTIERS IN AGING NEUROSCIENCE
ISSN	1663-4365
卷号	14
产权排序	1
摘要	Detection of early morphological changes in the brain and early diagnosis are important for Alzheimer's disease (AD), and high-resolution magnetic resonance imaging (MRI) can be used to help diagnose and predict the disease. In this paper, we proposed two improved ResNet algorithms that introduced the Contextual Transformer (CoT) module, group convolution, and Channel Shuffle mechanism into the traditional ResNet residual blocks. The CoT module is used to replace the 3 x 3 convolution in the residual block to enhance the feature extraction capability of the residual block, while the Channel Shuffle mechanism is used to reorganize the feature maps of different groups in the input layer to improve the communication between the feature maps from different groups. Images of 503 subjects, including 116 healthy controls (HC), 187 subjects with mild cognitive impairment (MCI), and 200 subjects with AD, were selected and collated from the ADNI database, and then, the data were pre-processed and sliced. After that, 10,060 slices were obtained and the three groups of AD, MCI and HC were classified using the improved algorithms. The experiments showed that the refined ResNet-18-based algorithm improved the top-1 accuracy by 2.06%, 0.33%, 1.82%, and 1.52% over the traditional ResNet-18 algorithm for four medical image classification tasks, namely AD: MCI, AD: HC, MCI: HC, and AD: MCI: HC, respectively. The enhanced ResNet-50-based algorithm improved the top-1 accuracy by 1.02%, 2.92%, 3.30%, and 1.31%, respectively, over the traditional ResNet-50 algorithm in four medical image classification tasks, demonstrating the effectiveness of the CoT module replacement and the inclusion of the channel shuffling mechanism, as well as the competitiveness of the improved algorithms.
关键词	Alzheimer's disease MRI CoT module Channel Shuffle ResNet medical image classification
DOI	10.3389/fnagi.2022.930584
收录类别	SCI
语种	英语
WOS记录号	WOS:000885746600001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/96241
专题	光谱成像技术研究室
推荐引用方式 GB/T 7714	Li, Chao,Wang, Quan,Liu, Xuebin,et al. An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer's Disease Levels[J]. FRONTIERS IN AGING NEUROSCIENCE,2022,14.
APA	Li, Chao,Wang, Quan,Liu, Xuebin,&Hu, Bingliang.(2022).An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer's Disease Levels.FRONTIERS IN AGING NEUROSCIENCE,14.
MLA	Li, Chao,et al."An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer's Disease Levels".FRONTIERS IN AGING NEUROSCIENCE 14(2022).

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