Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things
Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things
复制标题
RLDD：IIoT 中针对 COVID-19 的高级残差学习诊断检测系统
DOI：
10.1109/tii.2021.3051952
复制
发表时间：
2021-09-01
影响因子：
12.3
通讯作者：
Xiong, Naixue
中科院分区：
文献类型：
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作者：
Zhang, Mingdong;Chu, Ronghe;Xiong, Naixue
由于传播速度快，健康损害严重，COVID-19引起了全球的关注。早诊断、早隔离是疫情防控的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），这既昂贵又耗时。为了建立一个高效和有效的替代NAT，本文探讨了采用肺部计算机断层扫描图像作为诊断信号的可行性。与正常肺不同，感染COVID-19的部分肺出现病变，毛玻璃样阴影和支气管扩张变得明显。通过公开数据集，在这篇文章中，我们提出了一种用于COVID-19技术的高级残差学习诊断检测（RLDD）方案，该方案旨在从异质肺部图像中区分阳性COVID-19病例。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。在测试集上，我们达到了91.33%的准确率，91.30%的精确度，和90%的召回率。对于150个样品的批次，评估时间仅为4.7 s。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。
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.