Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

2019 年冠状病毒病或 COVID-19 是一种迅速传播的病毒感染，已影响到全世界数百万人。随着疾病的迅速传播和人数的不断增加，医护人员越来越难以快速诊断疾病并遏制其传播。因此，自动化诊断过程已成为必要。这将提高工作效率并确保医护人员免受病毒感染。医学图像分析是可以更准确地解决这个问题的新兴研究领域之一。本文对使用最新的深度学习模型（VGG16、VGG19、DenseNet121、Inception-ResNet-V2、InceptionV3、Resnet50 和 Xception）处理肺炎病例中冠状病毒肺炎的检测和分类进行了比较研究。本研究使用了 7165 张 COVID-19 (1536) 和肺炎 (5629) 患者的胸部 X 光图像。使用混淆指标和性能指标来分析每个模型。结果显示，与本研究中的其他模型相比，DenseNet121（准确率 99.48%）表现出更好的性能。