# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 5 Discussion  
We proposed a deep learning methodology that can be used to effectively classify COVID-19 CXR images. The MIMIC-IV CXR and Mendeley datasets were used, and effective CLAHE was applied for detecting COVID-19 X-ray lesions. The classification model demonstrated improvements in the accuracy and generalization performance of the classification of COVID-19 CXR images through domain extension transfer learning based on ResNet-50. The classification model showed high-performance levels, with an accuracy of 96.7%, precision level of 96.2%, sensitivity level of 96.1%, specificity level of 97.2%, and an F1-score of 96.1%. As a result of applying domain extension transfer learning and the ImageNet pretrained ResNet-50 model, the performance of the classification model in terms of accuracy improved by 9.9%, precision improved by 13.1%, sensitivity by 9.6%, specificity by 10.2%, and the F1-score by 11.9%, when domain extension transfer learning was applied on the MIMIC CXR(PA) data.  
To show that the proposed methodology is effective, we carried out comparisons of it with six state-of-the-art studies: Dark-CovidNet [20], CovidXNet [24], RadiomiX [18], DenseNet-121 [46], Inception-ResNet-v2 [47], and U-Net [48]. We also compared the proposed methodology with the study with the same dataset [19]. Among all the methods, the proposed methodology showed the highest performance of 97.2% in specificity, which is one of the important criteria in biomedical image classification.  
Using Grad-CAM, one of the XAI methodologies, the numerical results of the proposed methodology were visualized to interpret the results of the performance of the model. A COVID-19 CXR image obtained from the Korean Society of Thoracic Radiology was cited and applied to Grad-CAM. Grad-CAM characterized the lung lesions, and the radiologist findings were used to confirm the superiority of the classification model. Furthermore, by verifying the reliability of the model performance through statistical tests, we showed that the proposed methodology can be used to effectively classify COVID-19 CXR images.  
The results of applying the proposed methodology for matching the source domain and the target domain are as follows. The pretraining was conducted with the MIMIC-IV dataset without COVID-19 X-ray images. A fine-tuning strategy was performed to classify the COVID-19 images and non-COVID-19 images well. It was confirmed that the proposed methodology is effective through model performance figures and visual results.  
We believe that if another new pandemic with similar lung diseases breaks out, we could apply the proposed methodology to a new scarce CXR image dataset. Therefore, we could quickly respond to screening for new viruses and prevent the spreading of viruses in the local society. In addition, by applying the proposed methodology not only would a quick quarantine response be possible but also a reduction in the burden of a large expenditure to support professionals, expensive inspection kits, and inspection sites. Moreover, the proposed methodology is able to provide visualized information to medical staff and professionals to help them to make good decisions regarding the level of treatment. Therefore, through the proposed methodology, we can build up a proper medical system for patients and the local society.  
Recently, COVID-19 lesion detection and classification studies have been conducted using CT and MRI images as well as X-rays. The related studies have achieved more than 95% of the state-of-the-art. In future research, we will apply the domain extension transfer learning presented in this study to the areas of CT and MRI to confirm its applicability in the overall medical image classification field.