An open auscultation dataset for machine learning-based respiratory diagnosis studies.

IF 1.2 Q3 ACOUSTICS
Guanyu Zhou, Chengjian Liu, Xiaoguang Li, Sicong Liang, Ruichen Wang, Xun Huang
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引用次数: 0

Abstract

Machine learning enabled auscultating diagnosis can provide promising solutions especially for prescreening purposes. The bottleneck for its potential success is that high-quality datasets for training are still scarce. An open auscultation dataset that consists of samples and annotations from patients and healthy individuals is established in this work for the respiratory diagnosis studies with machine learning, which is of both scientific importance and practical potential. A machine learning approach is examined to showcase the use of this new dataset for lung sound classifications with different diseases. The open dataset is available to the public online.

用于基于机器学习的呼吸诊断研究的开放式听诊数据集。
机器学习支持的听诊诊断可提供有前景的解决方案,尤其是用于预筛选目的。其潜在成功的瓶颈在于用于训练的高质量数据集仍然稀缺。本研究建立了一个开放的听诊数据集,该数据集由病人和健康人的样本和注释组成,用于机器学习的呼吸诊断研究,具有重要的科学意义和实用潜力。本文研究了一种机器学习方法,以展示如何将这一新数据集用于不同疾病的肺部听诊分类。该开放数据集可供公众在线使用。
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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CiteScore
1.70
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0.00%
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