## 4 Experimental Results
### Attention Analysis  
The two different categories of experimentation have undergone attention analysis. The same dataset was used for training and testing in the first set of experiments. Figure 12 shows the proposed model's classification and attention accuracy on the trained and tested Kaggle dataset. Figure 13 shows the results of the proposed model when trained and tested on the FigShare dataset. The attention shows MRI images in pairs with actual and expected labels for glioma, meningioma, pituitary, and no-tumor cases. These results show that the model can accurately detect and predict tumor locations, as shown by the strong label alignment. This evaluation indicates that the model's resilience and consistency on the dataset it was trained on are sufficient for tumor classification and detection in the same area. We also examined the impact of cross-dataset validation, where the proposed model is trained on different MRI images while testing on completely unseen data. Three types of attention analysis are carried out in this work. In the first analysis, the proposed model is trained on the FigShare dataset and tested on the Kaggle Dataset. The proposed model's generalization capability across datasets is demonstrated in Figure 14, showing the results of its training on the FigShare dataset and testing on the Kaggle dataset. The figure illustrates pairings of MRI scans, including the actual label and its corresponding computed label. In distinct cases, the three classes of brain tumor--glioma, pituitary, and meningioma--are illustrated, along with their respective actual and predicted segmentation or classification results. Specific instances from the dataset are highlighted by the  
Figure 12: Attention analysis of the proposed model when trained and tested on the Kaggle dataset. Figure 13: Attention analysis of the proposed model—training and testing dataset: FigShare. dataset (e.g., Tr-gl_0165) in each case. The precise overlap between the actual and predicted tumor regions in the images illustrates the model's efficacy in cross-dataset prediction. Despite the domain disparities between the training and testing datasets, this visualization demonstrates the model's ability to handle unseen data during testing. The proposed model's performance is demonstrated in Figure 15, where the training has been performed on the Kaggle dataset and subsequently testing on the BraTS2019 and BraTS2020 datasets. The MRI image, the ground truth segmentation mask, and the corresponding attention map generated by the proposed model are all presented in three columns. The attention maps emphasize the regions the model identified as relevant for segmentation and classification, demonstrating a significant overlap with the ground truth segmentation. The model accurately segments tumor regions across various test instances in the BraTS2019 and BraTS2020 datasets, as evidenced by the close correspondence between the attention maps and the ground truth masks. The results suggest that the model can accommodate tumor morphologies, sizes, and variations in intensity, despite training on a distinct dataset. This analysis emphasizes the model's capacity to effectively adjust data from various medical image repositories that are not visible. Medical imaging quality affects the brain tumor classification model performance. Despite deep learning advances, limited resolution, noise, and distortions in MRI scans can conceal tumor features and hamper categorization. Super-resolution methods, especially those based on generative adversarial networks (GANs), can improve image clarity and feature extraction. Recent studies like [42] have shown that AI-driven super-resolution approaches can improve medical imaging resolution while preserving structural integrity. Super-resolution can show finer tumor characteristics by producing high-resolution MRI scans from low-resolution ones, improving categorization. Such solutions in our pipeline could enhance our augmentation efforts and improve GATransformer model feature representation.