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Acoustery System for Differential Diagnosing of Coronavirus COVID-19 Disease
IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2021-11-13 , DOI: 10.1109/ojemb.2021.3127078
Anastasia Mitrofanova 1 , Dmitry Mikhaylov 2 , Ilman Shaznaev 3 , Vera Chumanskaia 4 , Valeri Saveliev 5
Affiliation  

Goal: Because of the outbreak of coronavirus infection, healthcare systems are faced with the lack of medical professionals. We present a system for the differential diagnosis of coronavirus disease, based on deep learning techniques, which can be implemented in clinics. Methods: A recurrent network with a convolutional neural network as an encoder and an attention mechanism is used. A database of about 3000 records of coughing was collected. The data was collected through the Acoustery mobile application in hospitals in Russia, Belarus, and Kazakhstan from April 2020 to October 2020. Results: The model classification accuracy reaches 85%. Values of precision and recall metrics are 78.5% and 73%. Conclusions: We reached satisfactory results in solving the problem. The proposed model is already being tested by doctors to understand the ways of improvement. Other architectures should be considered that use a larger training sample and all available patient information.

中文翻译:

用于鉴别诊断冠状病毒 COVID-19 疾病的声学系统

目标:由于冠状病毒感染的爆发,医疗保健系统面临缺乏医疗专业人员的问题。我们提出了一个基于深度学习技术的冠状病毒疾病鉴别诊断系统,该系统可以在诊所中实施。方法:使用卷积神经网络作为编码器和注意力机制的循环网络。收集了大约 3000 条咳嗽记录的数据库。这些数据是通过 Acoustery 移动应用程序在 2020 年 4 月至 2020 年 10 月期间在俄罗斯、白俄罗斯和哈萨克斯坦的医院收集的。结果:模型分类准确率达到85%。精度和召回指标的值为 78.5% 和 73%。结论:我们在解决问题方面取得了令人满意的结果。医生已经对所提出的模型进行了测试,以了解改进的方法。应考虑使用更大训练样本和所有可用患者信息的其他架构。
更新日期:2021-12-10
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