The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.

由于检测试剂盒数量有限，COVID-19 感染正在迅速增加。因此，COVID-19 检测试剂盒的开发仍然是一个开放的研究领域。最近，许多研究表明，胸部计算机断层扫描 (CT) 图像可用于 COVID-19 检测，因为胸部 CT 图像显示 COVID-19 感染患者的双侧变化。然而，从胸部 CT 图像中对 COVID-19 患者进行分类并非易事，因为预测双侧变化被定义为不适定问题。因此，在本文中，使用深度迁移学习技术对 COVID-19 感染患者进行分类。此外，还利用具有成本敏感属性的 top-2 平滑损失函数来处理嘈杂和不平衡的 COVID-19 数据集类型的问题。