A deep ensemble learning-based automated detection of COVID-19 using lung CT images and Vision Transformer and ConvNeXt - Tian, Geng; Wang, Ziwei; Wang, Chang; Chen, Jianhua; Liu, Guangyi; Xu, He; Lu, Yuankang; Han, Zhuoran; Zhao, Yubo; Li, Zejun; Luo, Xueming; Peng, Lihong (Frontiers in Microbiology, 2022)
Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.