BACKGROUND:The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significantstep to fight against this virus as well as release pressure off the healthcare system. OBJECTIVE:One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. METHODS:Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. RESULTS:A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. CONCLUSION:This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

中文翻译：

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使用深度学习从胸部 X 射线图像中识别 COVID-19 样本：迁移学习方法的比较。


背景：2019 年新型冠状病毒病（COVID-19）构成全球突发公共卫生事件。感染人数和死亡人数每天都在激增，这给我们的社会和医疗系统带来了巨大压力。快速检测 COVID-19 病例是对抗这种病毒以及缓解医疗保健系统压力的重要一步。目的：COVID-19大流行迅速蔓延的关键因素之一是临床检测时间过长。胸部 X 光检查 (CXR) 等成像工具可以加快识别过程。因此，我们的目标是开发一种自动化 CAD 系统，用于使用 CXR 图像检测健康病例和肺炎病例中的 COVID-19 样本。方法：由于 COVID-19 基准数据集的稀缺，我们采用了深度迁移学习技术，检查了 15 种不同的预训练 CNN 模型，以找到最适合此任务的模型。结果：总共使用了 860 张图像（260 个 COVID-19 病例、300 个健康病例和 300 个肺炎病例）来研究所提出算法的性能，其中每类图像的 70% 用于训练，15% 用于验证，剩下的就是测试。观察结果表明，VGG19 获得了最高的分类准确率 89.3%，平均准确率、召回率和 F1 分数分别为 0.90、0.89、0.90。结论：本研究证明了深度迁移学习技术使用 CXR 图像识别 COVID-19 病例的有效性。