## 4 Conclusions  
In this work, we have shown the benefits of using the chest X-ray images as an early COVID-19 screening method to have a faster and safer method of COVID-19 detection. We also created new chest X-ray datasets from the available open datasets and then we cleaned the input data by removing low-quality images to make our training dataset more balanced. The first dataset used for COVID-19, pneumonia, and normal classification was collected from the COVID CXR and Chest X-ray Pneumonia datasets, containing a total of 36,384 images, and the second dataset used for COVID-19 severity classification was collected from RICORD and RALO datasets, containing a total of 3282 images. We ran experiments on five deep learning models, which are convolution-based and transformer-based models. The results indicated that using chest X-ray images to detect and assess COVID-19 severity is a promising method since it produces a reliable inference performance. We could also see the pattern that the transformer-based models performed better than convolution-based models on all three metrics: precision, recall, and F1-score. In future work, we will apply more data augmentation techniques such as GAN [65] to augment input data for more accurate training models. We will also consider customizing deep learning models for the chest X-ray dataset to create more robust and stable models  
Figure 9: Training history of (**a**) DenseNet and (**b**) Hybrid EfficientNet-DOLO on the COVID-19 severity assessment task. for the COVID-19 detection and the COVID-19 severity assessment task to improve model performance. Our machine learning model currently performs well on a research scale but is not ready as a production solution. We hope that in the future we can gather more real case datasets to apply our machine learning system for practical diagnosis in clinical settings. Conceptualization, T.L.D. ; funding acquisition, S.-H.L. and K.-R.K.; investigation, T.L.D. ; methodology, T.L.D. ; project administration, S.-H.L., S.-G.K. and K.-R.K.; software, T.L.D. ; S.-H.L., S.-G.K. and K.-R.K.; supervision, S.-H.L., S.-G.K. and K.-R.K.; validation, S.-H.L., S.-G.K. and K.-R.K.; writing--original draft, T.L.D. ; writing--review and editing, T.L.D. and S.-H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Brain Korea 21 project (BK21). Not applicable. The COVID CXR dataset is available online at [[https://www.kaggle.com/datasets/andyczhao/covidx-corr2/discussion/251355](https://www.kaggle.com/datasets/andyczhao/covidx-corr2/discussion/251355)]([https://www.kaggle.com/datasets/andyczhao/covidx-corr2/discussion/251355](https://www.kaggle.com/datasets/andyczhao/covidx-corr2/discussion/251355)) (accessed on 20 November 2021). The Chest X-ray Images (Pneumonia) dataset is available online at [[https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)) (accessed on 20 November 2021). The RICORD dataset is available online at [[https://www.rsna.org/covid-19/COVID-19-RICORD](https://www.rsna.org/covid-19/COVID-19-RICORD)]([https://www.rsna.org/covid-19/COVID-19-RICORD](https://www.rsna.org/covid-19/COVID-19-RICORD)) (accessed on 15 December 2021). The RALO dataset is available online at [[https://zenodo.org/record/4634000#.YkUfiShByUk](https://zenodo.org/record/4634000#.YkUfiShByUk)]([https://zenodo.org/record/4634000#.YkUfiShByUk](https://zenodo.org/record/4634000#.YkUfiShByUk)) (accessed on 15 December 2021).