LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Fan Wang , Xiaochen Yuan , Yue Liu , Chan-Tong Lam
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引用次数: 0

Abstract

Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.
LungNeXt:利用增强型 Mel 光谱图进行肺音分类的新型轻量级网络
肺部听诊对于早期发现肺部疾病至关重要。对肺部杂音进行分类需要医学专家的专业辨别。本文详细介绍了 LungNeXt 的特点,这是一种专为肺部声音分析而设计的新型分类模型。此外,我们还提出了两种辅助方法:用于数据增强的 RandClipMix(RCM)和用于特征提取的增强型 Mel-Spectrogram (EMFE)。RCM 通过随机混合同一类别中的片段来创建新的偶然肺音,从而解决了数据不平衡的问题。EMFE 增强了频谱图中的特定频段,以突出偶然特征。这些贡献使 LungNeXt 实现了出色的性能。LungNeXt 优化整合了适当数量的 NeXt 块,确保了卓越的性能和轻量级的模型架构。我们在 SPRSound 数据集上对所提出的 RCM 和 EMFE 方法以及 LungNeXt 分类网络进行了评估。实验结果表明,在 SPRSound 的肺部声音五类任务中取得了 0.5699 的高分。具体来说,LungNeXt 模型的特点是效率高,只有 3.804M 个参数,计算复杂度为 0.659G FLOPS。这种轻便高效的模型尤其适合应用于电子听诊器后端处理设备,为医生和患者提供高效的诊断建议。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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