# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 1 Introduction
### Background
Since the pandemic, research on COVID-19 disease detection and patient diagnosis by classifying images, such as X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), using artificial intelligence (AI) has been actively conducted [21]. As COVID-19 spread worldwide, research was conducted to detect COVID-19 lesions on CXR images based on deep learning methodologies [16; 22; 23]. Owing to the severe lack of COVID-19 CXR images at the beginning of the pandemic, research predominantly focused on deep learning architectures and algorithms. To automatically identify COVID-19 lesions on CXRs, [PERSON] et al. [20] proposed Dark-CovidNet to identify COVID-19 lesions in CXR. In addition, [PERSON] et al. [24] proposed a CovXNet model to detect patterns of COVID-19 in the CXR images of patients. However, in these studies, the proposed models suffered from insufficient learning and testing owing to insufficient COVID-19 CXR data. These limitations significantly affected the generalization of the model performance. In most studies using CXR images to detect and classify COVID-19 lesions, deep learning algorithms are proposed to overcome the limitations of the insufficient COVID-19 CXR image dataset, or transfer learning methodologies that are pretrained with natural images are used. However, these methodologies have to create a new algorithm when a new lung disease pandemic occurs, and in addition, a model pretrained with natural images rather than medical images has no choice but to show a low classification accuracy. Therefore, we proposed an effective deep learning methodology for classifying COVID-19 CXR images. To effectively respond to a pandemic caused by a new lung disease such as COVID-19, it is necessary to train a model using a large dataset. However, in reality, we faced the problem of not being able to produce good results owing to the lack of COVID-19 data. To solve this, we used MIMIC-IV CXR, which contained information about 11 lung diseases, to match target domains with medical images rather than natural images to effectively classify images even without learning COVID-19 images between pretraining. We proposed a deep learning methodology based on domain extension transfer learning. We improved the classification accuracy and generalization performance of the model using notarized MIMIC-IV data preprocessing and effective X-ray CLAHE. Based on domain extension transfer learning, the ResNet-50 model was used to effectively classify the COVID-19 CXR images through pretraining and fine-tuning. The numerical results of the classification performance of the model were visualized and interpreted using gradient-weighted class activation mapping (Grad-CAM) on the COVID-19 X-ray images containing the opinion of a radiologist. The reliability of the performance of the model was also evaluated through statistical tests. The remainder of this paper is organized as follows. Section 2 describes the related works. Section 3 describes the dataset used in this study, the preprocessing methodology, and the deep learning methodology. Section 4 describes the hyperparameters applied to the proposed methodology, the performance evaluation indicators used in the results of the experiments, and the performance results of the final model. Section 5 presents the discussions, and Section 6 presents the conclusions and directions for future research.