## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c} \hline  & & **ViT** & **DEiT** & **PVT** & **CoAtNet** \\ \hline without DA & & 97.05\% & 94.88\% & 98.63\% & 99.50\% \\ \hline  & No XAI & 96.56\% & 96.51\% & 98.80\% & 99.48\% \\ DCGAN & Saliency & 95.64\% & 96.72\% & 99.01\% & **99.86\%** \\  & DeepLIFT & 95.47\% & **97.43\%** & 96.82\% & 99.34\% \\  & Gradient\(\odot\)Input & 95.78\% & 96.82\% & **99.20\%** & 99.50\% \\ \hline  & No XAI & 95.24\% & 97.05\% & 98.73\% & 99.74\% \\ RAGAN & Saliency & 95.50\% & **96.77\%** & **99.08\%** & 99.69\% \\  & DeepLIFT & 95.33\% & 96.42\% & 98.73\% & 98.02\% \\  & Gradient\(\odot\)Input & 95.19\% & 96.53\% & 98.94\% & **99.79\%** \\ \hline  & No XAI & 91.11\% & 95.99\% & 98.66\% & 99.60\% \\ WGAN-GP & Saliency & 96.32\% & **97.17\%** & 98.87\% & **99.81\%** \\  & DeepLIFT & 96.79\% & 96.75\% & 98.89\% & 99.17\% \\  & Gradient\(\odot\)Input & 95.19\% & 96.53\% & **98.94\%** & 99.79\% \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy metric for the LG dataset. \begin{table}
\begin{tabular}{l l c c c c} \hline  & & **ViT** & **DEiT** & **PVT** & **CoAtNet** \\ \hline without DA & & 75.00\% & 73.36\% & 77.27\% & 76.17\% \\ \hline  & No XAI & 72.21\% & 74.59\% & 77.84\% & 75.01\% \\ DCGAN & Saliency & 72.36\% & 74.78\% & 77.14\% & 74.49\% \\  & DeepLIFT & 72.04\% & 75.01\% & 76.50\% & 74.76\% \\  & Gradient\(\odot\)Input & 71.44\% & **75.14\%** & **78.36\%** & 74.03\% \\ \hline  & No XAI & 71.30\% & 73.38\% & 77.00\% & 72.77\% \\ RAGAN & Saliency & 72.91\% & **75.88\%** & 75.82\% & 75.21\% \\  & DeepLIFT & 71.75\% & 73.08\% & 76.98\% & 74.85\% \\  & Gradient\(\odot\)Input & 71.61\% & 75.12\% & **77.92\%** & 75.44\% \\ \hline  & No XAI & 72.86\% & 75.03\% & 77.14\% & 73.54\% \\ WGAN-GP & Saliency & 70.91\% & 74.54\% & 76.98\% & 72.94\% \\  & DeepLIFT & 73.51\% & 75.20\% & 76.63\% & 73.96\% \\  & Gradient\(\odot\)Input & 71.66\% & **77.42\%** & **77.77\%** & **76.59\%** \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy metric for the UCSB dataset. It is important to observe that to expand our investigation, new experiments can be performed with different models and datasets, including other types of medical images. On the other hand, incorporating more datasets from various medical fields poses significant challenges, especially when considering GAN architectures. For instance, an architecture that works well for one type of medical image may not be optimal for another, requiring extensive experimentation and customization to achieve the best results. In addition, acquiring and processing datasets from different medical domains involves overcoming various logistical, ethical, and technical hurdles, which adds to the complexity of such an expansion. In this context, our approach opens up new possibilities for enhancing data augmentation techniques and improving the overall performance of Transformer-based models in histopathological datasets. It provides new patterns and insights for specialists interested in machine learning. Finally, we recognize the importance of discussing the computational complexity of the methods employed. However, performing a complexity analysis of algorithms, especially when integrating XAI with GANs, can be challenging due to the intricate nature of these models and the variability in computational demands across different setups. Moreover, the primary focus of our research was to explore the integration of XAI techniques with GANs to improve the quality of generated images rather than to provide a comprehensive analysis of the computational complexity. Despite the complexity analysis challenges, we consciously used gradient-based XAI methods in our research. Gradient-based XAI is the fastest among the various XAI types, making it a practical choice for our study. These methods work by calculating gradients concerning the input features, which helps to identify the input areas that most influence the model's output.