计算机科学
呼吸音
人工智能
多层感知器
模式识别(心理学)
卷积神经网络
听诊
语音识别
机器学习
人工神经网络
医学
内科学
放射科
哮喘
作者
Jane Saldanha,Shaunak Chakraborty,Shruti Patil,Ketan Kotecha,S. Anupama Kumar,Anand Nayyar
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2022-08-12
卷期号:17 (8): e0266467-e0266467
被引量:24
标识
DOI:10.1371/journal.pone.0266467
摘要
Computerized auscultation of lung sounds is gaining importance today with the availability of lung sounds and its potential in overcoming the limitations of traditional diagnosis methods for respiratory diseases. The publicly available ICBHI respiratory sounds database is severely imbalanced, making it difficult for a deep learning model to generalize and provide reliable results. This work aims to synthesize respiratory sounds of various categories using variants of Variational Autoencoders like Multilayer Perceptron VAE (MLP-VAE), Convolutional VAE (CVAE) Conditional VAE and compare the influence of augmenting the imbalanced dataset on the performance of various lung sound classification models. We evaluated the quality of the synthetic respiratory sounds’ quality using metrics such as Fréchet Audio Distance (FAD), Cross-Correlation and Mel Cepstral Distortion. Our results showed that MLP-VAE achieved an average FAD of 12.42 over all classes, whereas Convolutional VAE and Conditional CVAE achieved an average FAD of 11.58 and 11.64 for all classes, respectively. A significant improvement in the classification performance metrics was observed upon augmenting the imbalanced dataset for certain minority classes and marginal improvement for the other classes. Hence, our work shows that deep learning-based lung sound classification models are not only a promising solution over traditional methods but can also achieve a significant performance boost upon augmenting an imbalanced training set.
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