Research on Lung Sound Signal Image Feature Recognition Based on Temporal and Spatial Dual-Channel Long- and Short-Term Memory Model

IF 2.5 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2025-06-23 DOI:10.1002/ima.70141

Li Xueri, Hu Ruo, Xu Hong, Zhao Huimin

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Abstract

In this paper, through the study on the transformation of lung sound signal into image feature signal processing, we further mastered the processing process of lung sound signal, and used the new neural network model to identify and diagnose the image features of lung sound, effectively improving the effect of clinical AI-assisted diagnosis. To solve the problem that the traditional neural network model cannot obtain the temporal and spatial characteristics of lung sound signals at the same time, we propose a DCCLSTM (Dual-Channel Convolutional neural network for Long- and Short-Time Memory) to obtain spatial information and temporal information features of lung sound simultaneously. New features are generated by weighted fusion, which can effectively make up for the problem that the resolution of the feature map extracted by the traditional neural network model is reduced. This report presents the results of studies conducted on the lung sound dataset, and the accuracy rate of Dalal_CNN with the best effect was 89.56%. The DCCLSTM proposed in this study has a recognition accuracy of 97.40%. Experiments show that the DCCLSTM method is more accurate than the Dalal_CNN method.

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基于时空双通道长短期记忆模型的肺声信号图像特征识别研究

本文通过对肺音信号转化为图像特征信号处理的研究，进一步掌握了肺音信号的处理过程，并利用新的神经网络模型对肺音的图像特征进行识别和诊断，有效提高了临床人工智能辅助诊断的效果。为解决传统神经网络模型无法同时获取肺声信号时空特征的问题，提出了一种双通道长短时记忆卷积神经网络（DCCLSTM）来同时获取肺声的空间信息和时间信息特征。通过加权融合生成新的特征，有效地弥补了传统神经网络模型提取的特征图分辨率降低的问题。本报告给出了对肺音数据集的研究结果，其中效果最好的Dalal_CNN准确率为89.56%。本研究提出的dclstm识别准确率为97.40%。实验表明，DCCLSTM方法比Dalal_CNN方法更准确。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.