Aly Mohammed, Alotaibi Nouf Saeed
Department of Artificial Intelligence, Faculty of Computers and Artificial Intelligence, Egyptian Russian University, Badr City, 11829, Cairo, Egypt.
Department of Computer Science, College of Science, Shaqra University, Shaqra City, 11961, Saudi Arabia.
Inform Med Unlocked. 2022;32:101049. doi: 10.1016/j.imu.2022.101049. Epub 2022 Aug 13.
The goal of this paper is to classify the various cough and breath sounds of COVID-19 artefacts in the signals from dynamic real-life environments. The main reason for choosing cough and breath sounds than other common symptoms to detect COVID-19 patients from the comfort of their homes, so that they do not overload the Medicare system and therefore do not unwittingly spread the disease by regularly monitoring themselves. The presented model includes two main phases. The first phase is the sound-to-image transformation, which is improved by the Mel-scale spectrogram approach. The second phase consists of extraction of features and classification using nine deep transfer models (ResNet18/34/50/100/101, GoogLeNet, SqueezeNet, MobileNetv2, and NasNetmobile). The dataset contains information data from almost 1600 people (1185 Male and 415 Female) from all over the world. Our classification model is the most accurate, its accuracy is 99.2% according to the SGDM optimizer. The accuracy is good enough that a large set of labelled cough and breath data may be used to check the possibility for generalization. The results demonstrate that ResNet18 is the best stable model for classifying cough and breath tones from a restricted dataset, with a sensitivity of 98.3% and a specificity of 97.8%. Finally, the presented model is shown to be more trustworthy and accurate than any other present model. Cough and breath study accuracy is promising enough to put extrapolation and generalization to the test.
本文的目标是对来自动态现实环境信号中的新冠病毒假象的各种咳嗽声和呼吸声进行分类。选择咳嗽声和呼吸声而非其他常见症状来在家中舒适地检测新冠患者的主要原因是，这样不会使医保系统不堪重负，从而避免因定期自我监测而无意中传播疾病。所提出的模型包括两个主要阶段。第一阶段是声像转换，通过梅尔尺度频谱图方法进行了改进。第二阶段包括特征提取和使用九个深度迁移模型（ResNet18/34/50/100/101、GoogLeNet、SqueezeNet、MobileNetv2和NasNetmobile）进行分类。该数据集包含来自世界各地近1600人的信息数据（1185名男性和415名女性）。我们的分类模型是最准确的，根据SGDM优化器，其准确率为99.2%。该准确率足够高，以至于可以使用大量带标签的咳嗽和呼吸数据来检验泛化的可能性。结果表明，ResNet18是用于从受限数据集中对咳嗽声和呼吸音进行分类的最佳稳定模型，灵敏度为98.3%，特异性为97.8%。最后，所提出的模型被证明比任何其他现有模型更值得信赖且更准确。咳嗽和呼吸研究的准确率很有前景，足以进行外推和泛化测试。
