Liu Xiangbin, Wu Wenqian, Chun-Wei Lin Jerry, Liu Shuai
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081, Changsha, China.
College of Information Science and Engineering, Hunan Normal University; 410081, Changsha, China.
Curr Med Imaging. 2023;19(4):333-346. doi: 10.2174/1573405618666220610093740.
The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to develop a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method.
Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide an accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/ Pneumonia).
Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVIDensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multi-category classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images and that the performance of this model is superior to that of several existing methods.
Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.
由2019新型冠状病毒（COVID-19）引起的新型全球大流行疾病——新型冠状病毒肺炎，已在全球迅速传播，对日常生活、公共卫生安全和全球经济造成了巨大破坏。对COVID-19感染患者进行早期检测和治疗对于防止疫情进一步传播至关重要。然而，现有的检测方法无法快速检测出COVID-19患者，导致感染个体不能及时被发现，这在一定程度上使COVID-19的防控工作变得复杂。因此，开发一种快速实用的COVID-19检测方法至关重要。在这项工作中，我们探索了深度学习在COVID-19检测中的应用，以开发一种快速的COVID-19检测方法。
现有研究表明，新型冠状病毒肺炎具有显著的影像学表现。在本研究中，我们分析并选择胸部X光片的特征。我们提出了一种基于所选特征的胸部X光（CXR）分类方法，并研究迁移学习在检测肺炎和COVID-19中的应用。此外，我们将基于所选特征提出的CXR分类方法与迁移学习和集成学习相结合，提出了一种基于迁移学习的集成深度学习模型COVID-ensemble，用于使用胸部X光图像诊断肺炎和COVID-19。该模型旨在为二元分类（无异常/肺炎）和多变量分类（COVID-19/无异常/肺炎）提供准确诊断。
我们提出的基于选择特征的CXR分类方法可以显著提高CNN模型的CXR分类准确率。使用该方法，DarkNet19的二元和三元分类准确率分别提高了3.5%和5.78%。此外，COVID-ensemble在二元分类任务中的准确率达到了91.5%，在多类别分类任务中的准确率达到了91.11%。实验结果表明，COVID-ensemble可以通过X光图像快速准确地自动检测出COVID-19和肺炎，并且该模型的性能优于几种现有方法。
我们提出的COVID-ensemble不仅可以克服传统COVID-19检测方法RT-PCR的局限性，提供便捷快速的COVID-19检测，还能自动检测肺炎，从而减轻医护人员的压力。使用深度学习模型从X光图像中自动诊断COVID-19和肺炎可以作为一种快速有效的COVID-19和肺炎筛查方法。
