The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.

中文翻译：

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基于深度学习的基于胸部 X 射线图像的 COVID-19 分类方法的评估


COVID-19，即新型冠状病毒或 SARS-Cov-2，已夺去了数十万人的生命，影响了全世界数百万人，死亡和感染人数呈指数级增长。深度卷积神经网络（DCNN）对于包括医学图像在内的图像分类任务来说是一个巨大的里程碑。最先进模型的迁移学习已被证明是克服数据不足问题的有效方法。在本文中，对八个预训练模型进行了全面评估。这些模型的训练、验证和测试是在属于五个不同类别的胸部 X 射线 (CXR) 图像上进行的，总共包含 760 个图像。在 ImageNet 数据集中预先训练的微调模型计算高效且准确。微调后的 DenseNet121 在健康、细菌性肺炎、COVID-19 和病毒性肺炎四类分类中实现了 98.69% 的测试准确率和 0.99 的宏观 f1 分数，并且微调模型在三类分类中实现了更高的测试准确率包含健康、COVID-19 和 SARS 图像。实验结果表明，只有总参数的 62% 经过重新训练才能达到这样的精度。