Elgendi Mohamed, Nasir Muhammad Umer, Tang Qunfeng, Fletcher Richard Ribon, Howard Newton, Menon Carlo, Ward Rabab, Parker William, Nicolaou Savvas
School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Department of Obstetrics & Gynaecology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Front Med (Lausanne). 2020 Aug 18;7:550. doi: 10.3389/fmed.2020.00550. eCollection 2020.
Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.
胸部X线摄影是COVID-19肺炎早期检测、管理规划及随访评估的关键工具；然而，在全球各地较小的诊所中，缺乏放射科医生来分析大量检查，尤其是在疫情期间进行的检查。在患者周转量高的发展中国家和地区，高分辨率计算机断层扫描和实时聚合酶链反应的可用性有限，这也凸显了胸部X线摄影作为筛查和诊断工具的重要性。在本文中，我们比较了17种可用深度学习算法的性能，以帮助识别COVID-19肺炎的影像学特征。我们利用现有的诊断技术（胸部X线摄影）和预先存在的神经网络（DarkNet-19）来检测COVID-19肺炎的影像学特征。我们的方法省去了开发新技术和相关算法所需的额外时间和资源，从而在与COVID-19疫情的赛跑中帮助一线医护人员。我们的结果表明，DarkNet-19是检测COVID-19肺炎影像学特征的最佳预训练神经网络，在5854张X线图像上的总体准确率为94.28%。我们还展示了结果的定制可视化，可用于突出该疾病重要的视觉生物标志物和疾病进展情况。
