Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.

中文翻译：

---

残差学习诊断检测：工业物联网中针对COVID-19的先进残差学习诊断检测系统

由于传播速度快、健康损害严重，COVID-19引起了全球的关注。早期诊断、早期隔离是疫情防控有效且势在必行的策略。大多数 COVID-19 的诊断方法都是基于核酸检测 (NAT)，这种方法既昂贵又耗时。为了构建高效且有效的 NAT 替代方案，本文研究了采用肺部计算机断层扫描图像作为诊断信号的可行性。与正常肺部不同，感染了 COVID-19 的肺部部分出现病变、毛玻璃样混浊和支气管扩张变得明显。通过公共数据集，在本文中，我们提出了一种针对 COVID-19 技术的高级残差学习诊断检测（RLDD）方案，该方案旨在区分阳性 COVID-19 病例和异质肺部图像。除了诊断效率高的优点外，所设计的基于残差的COVID-19检测网络可以通过小规模的COVID-19样本有效地提取肺部特征，从而消除了对其他医学数据集的预训练要求。在测试集中，我们实现了 91.33% 的准确率、91.30% 的精确率和 90% 的召回率。对于150个样品的批次，评估时间仅为4.7秒。因此，RLDD可以集成到应用程序编程接口中并嵌入到医疗仪器中，以提高COVID-19的检测效率。