The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.

中文翻译：

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深度X射线胸片影像中新型冠状病毒分类的集成模型

新型冠状病毒SARS-CoV-2可能对人致命，导致COVID-19。它的易传播性，加上它在被感染者中的高致病性和死亡能力，使其对社区构成危害。胸部X光检查是早期诊断冠状病毒相关感染的最常见但最难解释的影像学检查之一。它们携带了大量的解剖和生理信息，但是即使对于放射线专家来说，有时也很难得出其中包含的相关信息。使用深度学习模型的自动分类可以帮助快速更好地评估这些感染。深度CNN模型（即MobileNet，ResNet50和InceptionV3）采用了不同的变体，包括从一开始就训练模型，在调整所有层的学习权重的同时进行微调，并在进行学习的同时对学习的权重进行微调。在预训练的模型中，使用增强进行微调可以产生最佳效果。其中，为整体学习选择的两个最佳性能模型（MobileNet和InceptionV3）产生的准确性和FScore分别为95.18％和90.34％，以及95.75％和91.47％。结合这些深层模型生成的拟议混合集成模型产生了96.49％和92.97％的分类精度和FScore。对于单独保存的测试数据集，该模型生成的准确度和FScore为94.19％和88.64％。使用深度合奏学习进行自动分类可以帮助放射科医生正确识别胸部X射线中与冠状病毒相关的感染。所以，