Singh Dilbag, Kumar Vijay, Kaur Manjit
Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, 201310 India.
Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India.
Appl Intell (Dordr). 2021;51(5):3044-3051. doi: 10.1007/s10489-020-02149-6. Epub 2021 Feb 7.
The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.
用于检测新型冠状病毒（COVID-19）的广泛使用的工具是实时聚合酶链反应（RT-PCR）。然而，RT-PCR试剂盒成本高昂且耗时较长，大约需要6到9小时才能将受试者分类为COVID-19阳性或COVID-19阴性。由于RT-PCR的灵敏度较低，它存在较高的假阴性结果。为了克服这些问题，文献中已经实现了许多深度学习模型用于疑似受试者的早期分类。为了解决与RT-PCR相关的灵敏度问题，利用胸部CT扫描将疑似受试者分类为COVID-19阳性、肺结核、肺炎或健康受试者。对COVID-19阳性受试者胸部CT扫描的广泛研究表明存在一些双侧变化和独特模式。但是对胸部CT扫描进行人工分析是一项繁琐的任务。因此，通过整合深度迁移学习模型，如密集连接卷积网络（DCCN）、ResNet152V2和VGG16，实现了一种自动化的COVID-19筛查模型。实验结果表明，所提出的集成模型在准确率、F值、曲线下面积、灵敏度和特异性方面优于竞争模型。
