Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi, China.
School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, China.
PLoS One. 2020 Nov 17;15(11):e0242535. doi: 10.1371/journal.pone.0242535. eCollection 2020.
A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.
一种新出现的冠状病毒（COVID-19）严重威胁着全世界人类的生命和健康。在应对和抗击 COVID-19 过程中，最关键的一步是有效地对感染患者进行筛查和诊断。其中，胸部 X 射线成像技术是一种有价值的影像学诊断方法。使用计算机辅助诊断来筛选 COVID-19 病例的 X 射线图像，可以为专家提供辅助诊断建议，在一定程度上减轻专家的负担。在这项研究中，我们首先使用常规的迁移学习方法，使用五个预先训练的深度学习模型，其中 Xception 模型效果比较理想，诊断准确率达到 96.75%。为了进一步提高诊断准确率，我们提出了一种有效的诊断方法，该方法结合了深度学习特征和机器学习分类，实现了端到端的诊断模型。该方法在两个数据集上进行了测试，效果都非常出色。我们首先在 1102 张胸部 X 射线图像上对模型进行了评估。实验结果表明，Xception+SVM 的诊断准确率高达 99.33%。与基线 Xception 模型相比，诊断准确率提高了 2.58%。该模型的灵敏度、特异性和 AUC 分别达到 99.27%、99.38%和 99.32%。为了进一步说明我们方法的稳健性，我们还在另一个数据集上测试了我们提出的模型。最后也取得了很好的结果。与相关研究相比，我们提出的方法具有更高的分类准确率和高效的诊断性能。总的来说，所提出的方法大大推进了当前基于放射学的方法，它可以成为临床医生和放射科医生非常有帮助的工具，帮助他们对 COVID-19 病例进行诊断和随访。
