Tactile edge technology that focuses on 5G or beyond 5G reveals an exciting approach to control infectious diseases such as COVID-19 internationally. The control of epidemics such as COVID-19 can be managed effectively by exploiting edge computation through the 5G wireless connectivity network. The implementation of a hierarchical edge computing system provides many advantages, such as low latency, scalability, and the protection of application and training model data, enabling COVID-19 to be evaluated by a dependable local edge server. In addition, many deep learning (DL) algorithms suffer from two crucial disadvantages: first, training requires a large COVID-19 dataset consisting of various aspects, which will pose challenges for local councils; second, to acknowledge the outcome, the findings of deep learning require ethical acceptance and clarification by the health care sector, as well as other contributors. In this article, we propose a B5G framework that utilizes the 5G network's low-latency, high-bandwidth functionality to detect COVID-19 using chest X-ray or CT scan images, and to develop a mass surveillance system to monitor social distancing, mask wearing, and body temperature. Three DL models, ResNet50, Deep tree, and Inception v3, are investigated in the proposed framework. Furthermore, blockchain technology is also used to ensure the security of healthcare data.

中文翻译：

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可解释的人工智能和基于大规模监控系统的医疗保健框架，用于对抗像大流行病这样的 COVID-I9


专注于 5G 或 5G 之外的触觉边缘技术揭示了一种在国际范围内控制 COVID-19 等传染病的令人兴奋的方法。通过 5G 无线连接网络利用边缘计算，可以有效地控制 COVID-19 等流行病。分层边缘计算系统的实施提供了许多优势，例如低延迟、可扩展性以及应用程序和训练模型数据的保护，使得可以通过可靠的本地边缘服务器来评估COVID-19。此外，许多深度学习（DL）算法存在两个关键缺点：首先，训练需要包含各个方面的大型COVID-19数据集，这会给地方议会带来挑战；其次，为了承认结果，深度学习的研究结果需要医疗保健部门以及其他贡献者的道德接受和澄清。在本文中，我们提出了一个 B5G 框架，利用 5G 网络的低延迟、高带宽功能，使用胸部 X 光或 CT 扫描图像来检测 COVID-19，并开发一个大规模监控系统来监控社交距离、口罩穿着情况、体温等。在所提出的框架中研究了三种深度学习模型：ResNet50、Deep tree 和 Inception v3。此外，区块链技术还用于确保医疗数据的安全。