Evidence map›Paper›PMID 39505340›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024

[A lung sound classification model with a spatial and channel reconstruction convolutional module].

N Ye, C Wu, J Jiang

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

N YeDepartment of Computer Science and Technology, College of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
C WuDepartment of Computer Science and Technology, College of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
J JiangDepartment of Computer Science and Technology, College of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo construct a model with a spatial and channel reconstruction convolutional module for accurate identification and classification of lung sound data.

methodsWe propose a convolutional network architecture combining the spatial-channel reconstruction convolution (SCConv) module. A lung sound feature extraction method combining the dual tunable Q-factor wavelet transform (DTQWT) with the triple Wigner-Ville transform (WVT) was used to improve the model's ability to capture the key features of the lung sounds by adaptively focusing on the important channel and spatial features. The performance of the model for classification of normal, crackles, wheezes, and crackles with wheezes was tested using the ICBHI2017 dataset. RESULTS AND

conclusionThe accuracy, sensitivity, specificity and F1 score of the proposed method reached 85.68%, 93.55%, 86.79% and 90.51%, respectively, demonstrating its good performance in classification tasks in the ICBHI2017 lung sound database, especially for distinguishing normal from abnormal lung sounds.

Indexed as

LungRespiratory SoundsAlgorithmsHumansNeural Networks, ComputerSensitivity and SpecificityWavelet Analysisconvolutional neural networkdual tunable Q-factor wavelet transformlung sound classificationspatial and channel reconstruction convolutiontriple Wigner-Ville transform

Identifiers

PMID39505340
PMCPMC11744096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.