## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & & **ViT** & **DEiT** & **PVT** & **CoAtNet** \\ \hline without DA & & 81.29\% & 87.89\% & 91.81\% & 93.19\% \\ \hline \multirow{4}{*}{DCGAN} & No XAI & 82.67\% & 87.48\% & 91.80\% & 92.30\% \\  & Saliency & **84.14\%** & **87.70\%** & **92.24\%** & **93.44\%** \\  & DeepLIFT & 79.97\% & 87.63\% & 91.48\% & 93.15\% \\  & Gradient\(\odot\)Input & 82.89\% & 87.25\% & 91.98\% & **93.44\%** \\ \hline \multirow{4}{*}{RAGAN} & No XAI & 83.53\% & **88.44\%** & 91.02\% & 92.73\% \\  & Saliency & 81.87\% & 88.33\% & 90.98\% & **93.88\%** \\  & DeepLIFT & **84.39\%** & 87.28\% & **91.92\%** & 93.06\% \\  & Gradient\(\odot\)Input & 80.56\% & 87.28\% & 91.86\% & 92.64\% \\ \hline \multirow{4}{*}{WGAN-GP} & No XAI & 81.60\% & 86.52\% & 91.15\% & 91.95\% \\  & Saliency & **84.41\%** & **88.09\%** & 91.98\% & **93.64\%** \\ \cline{1-1}  & DeepLIFT & 78.25\% & 87.91\% & 91.54\% & 93.36\% \\ \cline{1-1}  & Gradient\(\odot\)Input & 83.34\% & 87.96\% & **92.13\%** & 93.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy metric for the CR dataset. \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & & **ViT** & **DEiT** & **PVT** & **CoAtNet** \\ \hline without DA & & 95.04\% & 94.2\% & 96.44\% & 99.12\% \\ \hline \multirow{4}{*}{DCGAN} & No XAI & 93.14\% & 94.91\% & **97.75\%** & **99.32\%** \\  & Saliency & 92.29\% & **95.45\%** & 96.98\% & 98.93\% \\  & DeepLIFT & 93.46\% & 94.38\% & 97.16\% & 99.5\% \\  & Gradient\(\odot\)Input & 92.33\% & 93.48\% & 96.83\% & 99.26\% \\ \hline \multirow{4}{*}{RAGAN} & No XAI & 94.74\% & 94.34\% & 97.32\% & **99.33\%** \\  & Saliency & 92.61\% & 94.04\% & 97.42\% & 98.44\% \\ \cline{1-1}  & DeepLIFT & 94.00\% & 93.33\% & **97.71\%** & 99.22\% \\ \cline{1-1}  & Gradient\(\odot\)Input & 93.77\% & **94.98\%** & 97.65\% & 99.16\% \\ \hline \multirow{4}{*}{WGAN-GP} & No XAI & 94.04\% & 94.93\% & 97.05\% & 99.07\% \\ \cline{1-1}  & Saliency & 94.09\% & **97.79\%** & **98.24\%** & 98.77\% \\ \cline{1-1}  & DeepLIFT & 94.36\% & 97.46\% & 97.63\% & **99.34\%** \\ \cline{1-1}  & Gradient\(\odot\)Input & 94.73\% & 95.46\% & 97.56\% & 98.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy metric for the LA dataset. Based on the classification results, we analyzed the best combinations of XAI and GAN for the histological datasets. This analysis aimed to determine the number of cases in which the XGAN combinations provided the best performance given a base architecture. Table 6 shows a ranking of the best combinations. Considering all datasets, the combination XWGAN-GP + Saliency outperformed WGAN-GP in seven cases. The second and third places also included the Saliency method: XDCGAN + Saliency, with six cases; and XRAGAN + Saliency, with four cases. The subsequent three cases were combinations with the Gradient\(\odot\)Input and DeepLIFT methods: XWGAN-GP + Gradient\(\odot\)Input with five cases, XDCGAN + Gradient\(\odot\)Input with four cases, and XRAGAN + Gradient\(\odot\)Input and XRAGAN + DeepLIFT both with three cases. Finally, the two worst cases were combinations with DeepLIFT: XWGAN + DeepLIFT and XDCGAN + DeepLIFT, with only one case each. Based on these facts, the XAI method that provided the best information for the generator was Saliency, followed by Gradient\(\odot\)Input and DeepLIFT. These findings can guide researchers and experts in using GANs and XAI to develop artificial augmentation techniques.