# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 4 Results
### Xai
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author** & **Dataset** & **DL** & **All** & **COVID** & **Train** & **Validation** & **Test** & **Consitivity** & **Specificity** \\ \hline \multirow{3}{*}{Proposed} & \multirow{3}{*}{ResNet-50} & \multirow{3}{*}{ResNet-50} & 74, 101 & \multirow{3}{*}{5500} & \multirow{3}{*}{54,941/2426} & \multirow{3}{*}{7558/809} & \multirow{3}{*}{7558/809} & \multirow{3}{*}{96.1\%} & \multirow{3}{*}{**97.2\%**} \\  & & & & & & & & \\  & & & & & & & & \\ \hline \multirow{3}{*}{[19]} & \multirow{3}{*}{COVID-19} & 9545 & \multirow{3}{*}{5500} & \multirow{3}{*}{7636/4400} & \multirow{3}{*}{-/-} & \multirow{3}{*}{1909/1100} & \multirow{3}{*}{97.7\%} & \multirow{3}{*}{94.9\%} \\  & & & & & & & & \\  & & & & & & & & \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{COVID-19} & 9545 & \multirow{3}{*}{627} & \multirow{3}{*}{127} & \multirow{3}{*}{400/101} & \multirow{3}{*}{100/26} & \multirow{3}{*}{-/-} & \multirow{3}{*}{95.1\%} & \multirow{3}{*}{95.3\%} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{COVID-19} & 9545 & \multirow{3}{*}{636/4400} & \multirow{3}{*}{-/-} & \multirow{3}{*}{1909/1100} & \multirow{3}{*}{97.7\%} & \multirow{3}{*}{94.9\%} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \hline \multirow{3}{*}{[20]} & \multirow{3}{*}{COVID-19} & 945 & \multirow{3}{*}{627} & \multirow{3}{*}{127} & \multirow{3}{*}{400/101} & \multirow{3}{*}{100/26} & \multirow{3}{*}{-/-} & \multirow{3}{*}{95.1\%} & \multirow{3}{*}{95.3\%} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \hline \multirow{3}{*}{[24]} & \multirow{3}{*}{COVID-19} & 954 & \multirow{3}{*}{610} & \multirow{3}{*}{305} & \multirow{3}{*}{244/244} & \multirow{3}{*}{61/61} & \multirow{3}{*}{-/-} & \multirow{3}{*}{97.8\%} & \multirow{3}{*}{94.7\%} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & \\ \hline \multirow{3}{*}{[18]} & \multirow{3}{*}{RadioniX} & 1381 & \multirow{3}{*}{181} & \multirow{3}{*}{1104/145} & \multirow{3}{*}{-/-} & \multirow{3}{*}{276/36} & \multirow{3}{*}{78.9\%} & \multirow{3}{*}{91.0\%} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{DenseNet-121} & 2724 & \multirow{3}{*}{1029} & \multirow{3}{*}{1059/526} & \multirow{3}{*}{328/177} & \multirow{3}{*}{1337/326} & \multirow{3}{*}{84.0\%} & \multirow{3}{*}{93.0\%} \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \hline \multirow{3}{*}{[47]} & \multirow{3}{*}{Inception-} & 905 & \multirow{3}{*}{419} & \multirow{3}{*}{534/242} & \multirow{3}{*}{92/43} & \multirow{3}{*}{279/134} & \multirow{3}{*}{82.8\%} & \multirow{3}{*}{84.3\%} \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \hline \multirow{3}{*}{[48]} & \multirow{3}{*}{U-Net} & 5212 & \multirow{3}{*}{275} & \multirow{3}{*}{3285/657} & \multirow{3}{*}{597/120} & \multirow{3}{*}{1330/266} & \multirow{3}{*}{96.3\%} & \multirow{3}{*}{93.6\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of the proposed methodology with other studies. proposed methodology was applied clearly shows that there is no ground-glass opacity related to COVID-19 around the lungs. The COVID-19 X-ray represents a CXR diagnosed by a specialist showing that ground-glass opacity is spread over the entire left and right lungs of a woman in her 50s. The results visualized by the model are the same as those diagnosed by domain experts. The baseline model provided an interpretation with a slightly larger error than the model to which the proposed methodology was applied. The Mendeley data provided the lesion locations within the image. As a result of applying Grad-CAM using the information, the location of the lesion was detected from the Mendeley data.