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.

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费关键时间，大约需要 6 到 9 小时才能将受试者分类为 COVID-19(+) 或 COVID-19(-)。由于 RT-PCR 的敏感性较低，因此存在较高的假阴性结果。为了克服这些问题，文献中已经实施了许多深度学习模型，用于对可疑对象进行早期分类。为了处理与 RT-PCR 相关的敏感性问题，胸部 CT 扫描用于将疑似受试者分类为 COVID-19 (+)、肺结核、肺炎或健康受试者。对 COVID-19 (+) 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的模式。但是胸部 CT 扫描的手动分析是一项繁琐的任务。因此，通过集成深度迁移学习模型（如密集连接卷积网络 (DCCN)、ResNet152V2 和 VGG16）来实现自动化 COVID-19 筛选模型。实验结果表明，所提出的集成模型在准确性、f 度量、曲线下面积、灵敏度和特异性方面优于竞争模型。