The coronavirus disease, called COVID-19, which is spreading fast worldwide since the end of 2019, and has become a global challenging pandemic. Until 27th May 2020, it caused more than 5.6 million individuals infected throughout the world and resulted in greater than 348,145 deaths. CT images-based classification technique has been tried to use the identification of COVID-19 with CT imaging by hospitals, which aims to minimize the possibility of virus transmission and alleviate the burden of clinicians and radiologists. Early diagnosis of COVID-19, which not only prevents the disease from spreading further but allows more reasonable allocation of limited medical resources. Therefore, CT images play an essential role in identifying cases of COVID-19 that are in great need of intensive clinical care. Unfortunately, the current public health emergency, which has caused great difficulties in collecting a large set of precise data for training neural networks. To tackle this challenge, our first thought is transfer learning, which is a technique that aims to transfer the knowledge from one or more source tasks to a target task when the latter has fewer training data. Since the training data is relatively limited, so a transfer learning-based DensNet-121 approach for the identification of COVID-19 is established. The proposed method is inspired by the precious work of predecessors such as CheXNet for identifying common Pneumonia, which was trained using the large Chest X-ray14 dataset, and the dataset contains 112,120 frontal chest X-rays of 14 different chest diseases (including Pneumonia) that are individually labeled and achieved good performance. Therefore, CheXNet as the pre-trained network was used for the target task (COVID-19 classification) by fine-tuning the network weights on the small-sized dataset in the target task. Finally, we evaluated our proposed method on the COVID-19-CT dataset. Experimentally, our method achieves state-of-the-art performance for the accuracy (ACC) and F1-score. The quantitative indicators show that the proposed method only uses a GPU can reach the best performance, up to 0.87 and 0.86, respectively, compared with some widely used and recent deep learning methods, which are helpful for COVID-19 diagnosis and patient triage. The codes used in this manuscript are publicly available on GitHub at (https://github.com/lichun0503/CT-Classification).

新型冠状病毒肺炎（COVID-19）自2019年底以来在全球范围内快速传播，已成为一种全球性的具有挑战性的流行病。截至 2020 年 5 月 27 日，全球已有超过 560 万人感染，并导致超过 348,145 人死亡。医院已尝试使用基于CT图像的分类技术通过CT成像来识别COVID-19，旨在最大程度地减少病毒传播的可能性，减轻临床医生和放射科医生的负担。 COVID-19的早期诊断，不仅可以防止疾病进一步传播，还可以更合理地分配有限的医疗资源。因此，CT 图像在识别急需重症临床护理的 COVID-19 病例方面发挥着至关重要的作用。不幸的是，当前的突发公共卫生事件给收集大量精确数据用于训练神经网络造成了很大困难。为了应对这一挑战，我们的第一个想法是迁移学习，这是一种旨在当目标任务的训练数据较少时将知识从一个或多个源任务迁移到目标任务的技术。由于训练数据相对有限，因此建立了基于迁移学习的 DensNet-121 识别 COVID-19 的方法。所提出的方法受到CheXNet等前人用于识别常见肺炎的宝贵工作的启发，该方法使用大型胸部X光14数据集进行训练，该数据集包含14种不同胸部疾病（包括肺炎）的112,120张正面胸部X光片单独标记并取得了良好的性能。 因此，通过在目标任务中的小规模数据集上微调网络权重，将 CheXNet 作为预训练网络用于目标任务（COVID-19 分类）。最后，我们在 COVID-19-CT 数据集上评估了我们提出的方法。通过实验，我们的方法在准确率 (ACC) 和 F1 分数方面实现了最先进的性能。定量指标表明，与一些广泛使用的和最近的深度学习方法相比，该方法仅使用 GPU 即可达到最佳性能，分别高达 0.87 和 0.86，这有助于 COVID-19 诊断和患者分诊。本手稿中使用的代码可在 GitHub 上公开获取（https://github.com/lichun0503/CT-Classification）。