Research on lung sound classification model based on dual-channel CNN-LSTM algorithm

doi:10.1016/j.bspc.2024.106257

	Research on lung sound classification model based on dual-channel CNN-LSTM algorithm
	Zhang, Yipeng 1,2; Huang, Qiong 1; Sun, Wenhui 1,2; Chen, Fenlan 3; Lin, Dongmei4 ; Chen, Fuming 1
	2024-08
发表期刊	Biomedical Signal Processing and Control
ISSN	1746-8094
卷号	94
摘要	ulmonary diseases have a significant impact on human health and life safety, and abnormalities in the lungs are a direct response to lung diseases. Establishing an effective lung sound classification model that can assist in diagnosis is of great significance for electronic auscultation.In addressing the issue of lung sound signal classification, this study introduces a deep learning classification model based on a dual-channel CNN-LSTM algorithm. Initially, Mel-scale Frequency Cepstral Coefficients (MFCC) are employed for feature extraction from the dataset, transforming lung sound signals into Mel spectrograms. On this foundation, a dual-channel algorithm classification model is constructed, with parallel Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) modules. The CNN module is designed to capture spatial dimension features of the input data, while the LSTM module focuses on temporal dimension features. These two feature sets are fused together, enabling the model to classify lung sounds and thereby assisting in diagnosing pulmonary diseases for healthcare practitioners. This experiment used the ICBHI2017 Challenge Lungs dataset and obtained 5054 pieces of data through data augmentation and sampling techniques.The results show that the accuracy, recall, and F1 score of this model reach 99.01%, 99.13%, and 0.9915, respectively, significantly superior to other models, highlighting its practical application value. © 2024 The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army
关键词	Brain Computer aided diagnosis Convolution Convolutional neural networks Pulmonary diseases Cepstral coefficients Classification models Convolutional neural network Dual channel Long short-term memory network Lung sound classification Lung sounds Mel cepstral coefficient Memory network Sound classification
DOI	10.1016/j.bspc.2024.106257
收录类别	EI
语种	英语
出版者	Elsevier Ltd
EI入藏号	20241315828974
EI主题词	Long short-term memory
EI分类号	461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 716.1 Information Theory and Signal Processing ; 723.5 Computer Applications
原始文献类型	Journal article (JA)
EISSN	1746-8108
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/170241
专题	电气工程与信息工程学院
通讯作者	Chen, Fuming
作者单位	1.Medical Security Center, The 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army, Gansu, Lanzhou; 730050, China; 2.School of Information Engineering, Gansu University of Chinese Medicine, Gansu, Lanzhou; 730000, China; 3.Lanzhou Rail Transit Co., Ltd, Gansu, Lanzhou; 730051, China; 4.College of Electrical and Information Engineering, Lanzhou University of Technology, Gansu, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Zhang, Yipeng,Huang, Qiong,Sun, Wenhui,et al. Research on lung sound classification model based on dual-channel CNN-LSTM algorithm[J]. Biomedical Signal Processing and Control,2024,94.
APA	Zhang, Yipeng,Huang, Qiong,Sun, Wenhui,Chen, Fenlan,Lin, Dongmei,&Chen, Fuming.(2024).Research on lung sound classification model based on dual-channel CNN-LSTM algorithm.Biomedical Signal Processing and Control,94.
MLA	Zhang, Yipeng,et al."Research on lung sound classification model based on dual-channel CNN-LSTM algorithm".Biomedical Signal Processing and Control 94(2024).