## 4 Result Analysis and Discussion
### Comparison with Other Studies  
By comparing our study's findings to those of previous studies, we can observe that the technique we suggested, which involves combining the Swin Transformer (SwT) with ResNet50V2, achieves superior performance. Our approach achieved an outstanding accuracy of 99.9% when evaluated on the Br35H and Kaggle datasets as shown in Table 6, exceeding previous results. On the Kaggle dataset, ref. [51] achieved a 96% accuracy rate using a CNN, whereas [52] obtained a comparable accuracy rate of 95.7% using a similar approach. The experiments conducted with the Br35H dataset showed different success rates. In study [53], a convolutional neural network (CNN) achieved a success rate of 99.3%. Study [32] combined a CNN with GoogleNet and achieved a success rate of 99.5%. In study [54], EfficientNetb0 and VGG-19 were used, resulting in a success rate of 99.7%. In addition, ref. [55] utilized the InceptionV3 model with the Fisshare dataset and obtained an accuracy score of 97.12%. In addition, our results surpass the findings of [56], a research study that utilized convolutional neural networks (CNNs) on the Br35H dataset and achieved an accuracy of 99.3%. The effectiveness of the Swin Transformer, when used together with ResNet50V2, is demonstrated by the significant improvement in accuracy shown in our investigation. This indicates that the Swin Transformer holds significant  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & **Difference from** & **95\% Confidence** \\  & & **SwT+Resnet50V2** & **Interval** \\ \hline Resnet50V2 & 99 & 0.9 & (0.15, 1.65) \\ \hline VGG16 & 99 & 0.9 & (0.15, 1.65) \\ \hline MobileNetV2 & 98 & 1.9 & (1.15, 2.65) \\ \hline CNN & 98.6 & 1.3 & (0.55, 2.05) \\ \hline ConvNeXtTiny & 94.6 & 5.2 & (4.22, 6.18) \\ \hline EfficientNetV2B3 & 71.3 & 28.6 & (27.62, 29.58) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing the model’s accuracy with variations from SwT+Resnet50V2 and 95% confidence intervals for Bra35H dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy (\%)**} & **Difference from** & **95\% Confidence** \\  & & **SwT+Resnet50V2** & **Interval** \\ \hline Resnet50V2 & 91.1 & 5.7 & (79.02, 103.18) \\ \hline VGG16 & 70.9 & 25.8 & (58.82, 82.98) \\ \hline MobileNetV2 & 89.6 & 7.2 & (77.52, 101.68) \\ \hline CNN & 86.7 & 10. & (74.62, 108.88) \\ \hline ConvNeXtTiny & 69 & 27.8 & (56.92, 81.08) \\ \hline EfficientNetV2B3 & 34 & 62.8 & (21.92, 46.08) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing the models’ accuracy with variations from SwT+Resnet50V2 and 95% confidence intervals for Kaggle dataset.  
promise as an accurate option for image classification tasks in the field of medical imaging. The comprehensive evaluation demonstrates that our approach outperformed all previous studies, reaching a new standard for the accuracy of brain tumor classification.  
In addition, with proper validation and regulatory approval, such models could be integrated into routine imaging analysis software, facilitating earlier detection and personalized treatment planning. This approach significantly reduces patient burden by minimizing unnecessary biopsies and supplementary imaging. Future research may investigate the model's efficacy across diverse clinical environments and evaluate its compatibility with additional imaging techniques for enhanced applicability. This model could serve as an additional reader or assistive diagnostic tool, especially in high-volume clinical environments. Automating parts of the classification process may help reduce diagnostic errors, expedite clinical workflows, and prioritize cases requiring urgent intervention.