Investigation into the Classification of Cough Sounds for Early Asthma Screening.

IF 4.6 2区 医学 Q1 ALLERGY
Yanming Huo, Jiajing Ma, Huixian Liu, Luyuan Jia, Guo Zhang, Congkang Zhang, Xu Guo, Shen-Ao Hao, Yongdong Song, Haotian Sun
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引用次数: 0

Abstract

Purpose of review: This review aims to explore an effective and scalable approach for early asthma detection using cough sounds. The main objective is to evaluate whether a multi-model deep learning fusion framework can improve diagnostic accuracy and generalizability in real-world settings.

Recent findings: Recent research in respiratory sound analysis has demonstrated the potential of deep learning models in detecting pulmonary diseases. However, most studies focus on single-network architectures and often overlook class imbalance and training stability, which can limit model performance in practical applications. This study presents an asthma detection model that integrates ResNet18, VGG16, and DenseNet121 through a fusion layer. SMOTE is used to address data imbalance, and a weighted cross-entropy loss enhances training robustness. Mixed precision training and StepLR scheduling further improve performance. The proposed model achieved 95.9% accuracy on the test set, demonstrating strong generalization and potential for real-time, non-invasive screening in clinical environments.

咳嗽声分类对早期哮喘筛查的探讨。
综述目的:本综述旨在探索一种利用咳嗽声进行早期哮喘检测的有效且可扩展的方法。主要目的是评估多模型深度学习融合框架是否可以提高现实环境中的诊断准确性和泛化性。最近的发现:最近对呼吸声音分析的研究表明,深度学习模型在检测肺部疾病方面具有潜力。然而,大多数研究都集中在单网络架构上,往往忽略了类的不平衡和训练的稳定性,这在实际应用中会限制模型的性能。本研究提出了一种通过融合层集成ResNet18、VGG16和DenseNet121的哮喘检测模型。SMOTE用于解决数据不平衡问题,加权交叉熵损失增强了训练的鲁棒性。混合精度训练和StepLR调度进一步提高了性能。该模型在测试集上的准确率达到95.9%,显示出很强的泛化能力和在临床环境中进行实时、无创筛查的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.20
自引率
1.80%
发文量
21
审稿时长
6-12 weeks
期刊介绍: The aim of Current Allergy and Asthma Reports is to systematically provide the views of highly selected experts on current advances in the fields of allergy and asthma and highlight the most important papers recently published. All reviews are intended to facilitate the understanding of new advances in science for better diagnosis, treatment, and prevention of allergy and asthma. We accomplish this aim by appointing international experts in major subject areas across the discipline to review select topics emphasizing recent developments and highlighting important new papers and emerging concepts. We also provide commentaries from well-known figures in the field, and an Editorial Board of internationally diverse members suggests topics of special interest to their country/region and ensures that topics are current and include emerging research. Over a one- to two-year period, readers are updated on all the major advances in allergy and asthma.
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