DeepCS: Training a deep learning model for cervical spondylosis recognition on small-labeled sensor data

doi:10.1016/j.neucom.2021.11.008

	DeepCS: Training a deep learning model for cervical spondylosis recognition on small-labeled sensor data
	Wang, Nana 1,2,3,5; Luo, Chunjie 1,3; Huang, Xi 4; Huang, Yunyou 5; Zhan, Jianfeng 1,3
	2022-02-01
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	472 页码:24-34
摘要	Cervical spondylosis (CS) recognition systems provide regular screening services outside of a hospital and promote early detection and treatment of CS. However, in this paper, we propose a deep learning-based CS recognition system. Concerning the state-of-the-art and state-of-the-practice systems, the innovations of our approaches and algorithms are as follows: First, to elevate the reliance upon the sample number required for training the high-quality model, we reduce sample dimension and find optimal neural net-work architectures to reduce the number of model parameters to fit. Second, we incorporate multi-stream parallel network architecture search with multi-view feature extraction by converting time series classification into an image classification task. Specifically, five feature extraction methods (time-domain, frequency-domain, time-frequency domain, model-based, nonlinear feature extraction) are firstly uti-lized to extract features from multiple perspectives and form low-dimensional data set with multi-properties. Third, we reorganize low-dimensional data into image one representing the spatio-temporal relationship of muscle activity pattern. Finally, a multi-stream parallel network architecture search is proposed to use a bypass mechanism for optimal neural network architecture, each of which processes a kind of features mentioned above with an idea of the sparse connection of convolution neural network. The results on the real-world data set show that our CS recognition system achieves the average accuracy of 95.54%, average sensitivity of 99.09%, and average specificity of 90.00%, outperforming the state-of-the-art ones. (c) 2021 Elsevier B.V. All rights reserved.
关键词	Cervical spondylosis recognition High-dimensional time series sensor data Convolutional neural network Network architecture search Feature extraction
DOI	10.1016/j.neucom.2021.11.008
收录类别	SCI
语种	英语
资助项目	Project of Guangxi Science and Technology[GuiKeAD20297004]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000761893000003
出版者	ELSEVIER
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18965
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhan, Jianfeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol ICT, State Key Lab Comp Architecture, Beijing 100080, Peoples R China 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Software Syst Lab, ICT, ACS, Beijing 100080, Peoples R China 4.Chinese Acad Sci, Wireless Sensor Network Lab, ICT, Beijing, Peoples R China 5.Guangxi Normal Univ, Guilin, Peoples R China
推荐引用方式 GB/T 7714	Wang, Nana,Luo, Chunjie,Huang, Xi,et al. DeepCS: Training a deep learning model for cervical spondylosis recognition on small-labeled sensor data[J]. NEUROCOMPUTING,2022,472:24-34.
APA	Wang, Nana,Luo, Chunjie,Huang, Xi,Huang, Yunyou,&Zhan, Jianfeng.(2022).DeepCS: Training a deep learning model for cervical spondylosis recognition on small-labeled sensor data.NEUROCOMPUTING,472,24-34.
MLA	Wang, Nana,et al."DeepCS: Training a deep learning model for cervical spondylosis recognition on small-labeled sensor data".NEUROCOMPUTING 472(2022):24-34.