## 7 Ablation Study of the Proposed Model  
Ablation studies are crucial for deep-learning research [66]. Therefore, we adapted the dataset as chest X-ray (CXR) scans without any modification of the model, and we conducted an ablation study of the model to confirm its efficacy. A dataset [36] of 7132 CXR images in four categories, including COVID-19 (576), pneumonia (4273), tuberculosis (TB), and normal (1583), were ablated.  
Since the dataset was imbalanced due to an insignificant amount of COVID-19 and TB data, a stratified sampling was utilized, and each image was scaled to 150 \(\times\) 150 and [0, 1], correspondingly. The training, testing, and validation sets were randomly selected from the dataset according to a ratio of 80:10:10, respectively.  
For 15 epochs, the callbacks were monitored for losses for 3 epochs. The 10-fold result of the model is shown in Table 7. The model achieved a 96.07 \(\pm\) 0.34% training accuracy, a 0.1279 \(\pm\) 0.03 training loss, a 94.20 \(\pm\) 0.97% validation accuracy, a 0.2007 \(\pm\) 0.03 validation loss, a 93.50 \(\pm\) 1.39% test accuracy, and a 0.2160 \(\pm\) 0.047 test loss.  
The confusion matrix of the 10 th fold is shown in Figure 12. Only 5 COVID-19, 9 Normal, 15 pneumonia, and 10 TB images were misclassified, out of 69, 155, 423, and 67 images, respectively. Figure 13 shows the ROC-AUC curve that indicated the model supported the accurate diagnosis of COVID-19 and pneumonia.  
According to the results of the ablation study, the model could be applied to CXR pictures in order to diagnose conditions such as COVID-19, normal, TB, and pneumonia.  
\begin{table}
\begin{tabular}{l l l} \hline
**CT Image** & **Radiological Findings** & **Impressions** \\ \hline Figure 2a & Simple cystic lesion arising from right renal pelvis. & Right parapelvic cyst. \\ \hline Figure 2b & Both kidneys appear normal in size; parenchyma shows & Normal-appearing bilateral kidneys in the given section. \\ \hline Figure 2c & Radiopaque lesion visualized in right kidney. & Right renal calculus. \\ \hline Figure 2d & Heterogeneously enhancing lesion likely originating from & Malignant mass arising from left kidney. \\ \hline \end{tabular}
\end{table}
Table 6: Medical Sensation: Findings and Impressions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & **K1** & **K2** & **K3** & **K4** & **K5** & **K6** & **K7** & **K8** & **K9** & **K10** & **Avg (\(\mu\pm\sigma\))** \\ \hline TrA & 96.22 & 95.60 & 96.36 & 95.59 & 96.50 & 96.30 & 95.60 & 96.42 & 95.93 & 96.20 & 96.07 \(\pm\) 0.34 \\ \hline TrL & 0.1094 & 0.1360 & 0.1058 & 0.1278 & 0.1048 & 0.1120 & 0.1340 & 0.1589 & 0.1894 & 0.1012 & 0.1279 \(\pm\) 0.03 \\ \hline VaA & 95.93 & 92.85 & 94.53 & 92.99 & 94.24 & 95.22 & 92.91 & 94.41 & 94.13 & 94.78 & 94.20 \(\pm\) 0.97 \\ \hline VaL & 0.1281 & 0.1919 & 0.1804 & 0.2398 & 0.1964 & 0.2109 & 0.2034 & 0.2078 & 0.265 & 0.1831 & 0.2007 \(\pm\) 0.03 \\ \hline TsA & 94.26 & 89.92 & 94.40 & 92.30 & 94.54 & 94.30 & 93.12 & 94.55 & 93.30 & 94.36 & 93.50 \(\pm\) 1.39 \\ \hline TsL & 0.2009 & 0.2684 & 0.1618 & 0.2219 & 0.1589 & 0.1789 & 0.245 & 0.2719 & 0.2930 & 0.1680 & 0.2160 \(\pm\) 0.047 \\ \hline \end{tabular}
\end{table}
Table 7: The 10-fold training, testing and validation results of the proposed model in CXR dataset (after 15 epochs, in %): Training Accuracy (TrA),Training Loss (TrL), Validation Accuracy (VaA), Validation Loss (VaL), Test Accuracy (TsA) and Test Loss (TsL).