## 4 Results and Discussion
### Analysis and Comparison  
In this subsection, the remarks made during training are expressed. The comparison with three previous studies that trained with different strategies is exposed. The comparison of the performance of the proposed model is shown in Figure 13. It is clearly observed that the augmented image using the DCGAN approach with the Inception V3 model outperforms the other approach. Moreover, the comparison performance with the latest work is presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Approach** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline DCGAN & 96.53 & 95.78 & 94.57 & 95.4 \\ \hline DCGAN+Inception V3 & 97.86 & 97.6 & 97.43 & 97.2 \\ \hline DCGAN+XGradientBoost & 98.88 & 99.1 & 98.7 & 99.3 \\ \hline \end{tabular}
\end{table}
Table 3: Performance analysis with various approaches.  
Figure 12: Performance metric illustration of DCGAN-XGradientBoost with DCGAN.  
According to the findings presented in Table 4, a researcher employed the transfer learning methodology to categorize the chest X-ray pictures, resulting in an accuracy rate of 98.62%. This level of accuracy is comparable to the results reached by another researcher who utilized Covinet and CNN techniques. The AlexNet model has achieved an accuracy rate of 94.18% and a precision rate of around 93.4%. When comparing the suggested strategy to comparable methods, it is evident that PGGAN + SMANet achieves the highest accuracy (96.28%), with Wasserstein GAN coming in second (95.34%). The approach we have provided demonstrates compelling outcomes, achieving an accuracy rate of 98.88%, a precision score of 99.1%, a recall rate of 98.7%, and an F1 score of 98.60.  
The outcome is influenced by the enhancement of chest X-ray image quality using the DCGAN technique. The integration of the original and produced DCGAN data via InceptionV3 during training achieves a seamless mix. Additionally, XGradientBoost contributes to the process of selecting feature mappings. According to the data presented in Figure 14, our proposed solution has demonstrated superior performance compared to the other four methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Related Work** & **Dataset** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
[5] & Chest X-ray & Covinet System + CNN & 98.62 & 95.77 & 93.66 & 93.69 \\ \hline
[42] &
\begin{tabular}{c} Chest X-ray + Wasserstein \\ GAN \\ \end{tabular} & Wasserstein GAN & 95.34 & 99.1 & - & - \\ \hline
[43] & Chest X-ray + PGGAN & PGGAN + SMANet & 96.28 & - & - & - \\ \hline
[44] & Chest X-ray + GAN & DenseNet121 + GAN & 80.1 & 72.7 & 83.4 & 79.3 \\ \hline
[45] & Chest X-ray + IAGAN & Inception + IAGAN & 82 & 84 & 69 & - \\ \hline
[46] & Chest X-ray & Transfer Learning & 98.51 & 94.1 & 98.46 & 98.46 \\ \hline
[47] & Chest X-ray & ResNet34 \& HRNets & 97.02 & 95.6 & 98.41 & 96.98 \\ \hline
[48] & Chest X-ray & AlexNet mode & 94.18 & 93.4 & 89.1 & 98.9 \\ \hline Proposed Method &
\begin{tabular}{c} Chest X-ray and Augmented \\ Chest X-ray with DCGAN + \\ XGradientBoost \\ \end{tabular} & Inception V3 + DCGAN & 98.88 & 99.1 & 98.70 & 98.60 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison to other methods for chest X-ray classification.  
Figure 13: Accuracy rating comparison between DCGAN-XGradientBoost, DCGAN, and DCGAN + Inception V3.