## 4 Result Analysis and Discussion
### Statistical Analysis  
SwT+Resnet50V2 achieved the highest accuracy of 99.9%, outperforming the other models (Resnet50V2, VGG16, MobileNetV2, and CNN) by an accuracy margin of 0.9% to 1.9%, as demonstrated in Table 4. The confidence intervals for these differences provide additional information about their significance. The 95% confidence interval for the difference between Resnet50V2 and VGG16 ranges from 0.15% to 1.65%, indicating that the actual difference is unlikely to be of practical significance and is therefore insignificant. MobileNetV2 shows a 1.9% decrease in accuracy and a wider confidence interval of 1.15% to 2.65%, indicating a more significant variation in performance. The CNN model has a small variance of 1.3%, with a confidence range of 0.55% to 2.05%.  
Figure 5: Performance evaluation on Bra35H dataset.  
Figure 6: Training and validation metrics (accuracy and loss) for (SwT+Resnet50V2) on Bra35H dataset.  
Figure 7: Comparison of confusion matrices for all models using the Bra35H dataset.  
Overall, the results showed that SwT+Resnet50V2 performed slightly better than the other models, but the small confidence intervals for Resnet50V2 and VGG16 suggest that these differences might not be significant in real-world applications. However, MobileNetV2 shows a larger and more significant difference in accuracy. The confidence intervals provide a useful measure of the uncertainty around these differences, allowing for a more accurate interpretation of the comparative performance of the models. Compared to SwT+Resnet50V2, ConvNeXtTiny displays a more significant accuracy difference of 5.2%, with a 95% confidence interval of 4.22% to 6.18%, indicating a moderate change in the performance of the models.  
Figure 8: Performance evaluation on Kaggle dataset.  
Figure 9: Training and validation metrics (accuracy and loss) for (SwT+Resnet50V2) on Kaggle dataset.  
in performance. Compared to SwT+Resnet50V2, EfficientNetV2B3 exhibits the largest accuracy difference, measuring 28.6%, with a wide confidence range spanning from 27.62% to 29.58%. This suggests a significant difference in performance.  
SwT+Resnet50V2 is the top-performing model in the accuracy comparison, with an accuracy of 96.8% and a 95% confidence interval of (84.72%, 108.88%) as shown in Table 5. This demonstrates the model's robust and consistent performance. Resnet50V2 has an accuracy of 91.1%, behind SwT+Resnet50V2 by only 5.7%. Its confidence interval (79.02%, 103.18%) suggests some performance overlap. MobileNetV2 performs well within the confidence interval (77.52%, 101.68%), with an accuracy of 89.6% and a 7.2% difference. The CNN performs a bit worse than the other two, with an accuracy of 86.7% and a difference of 10.1%, as indicated by a confidence interval of 74.62% to 98.78%. Finally, VGG16 performs worse than SwT+Resnet50V2, with an accuracy of 70.9%, indicating a 25.9% difference. The confidence interval (58.82%, 82.98%) reflects the lower and less  
Figure 10: Comparison of confusion matrices for all models using the Kaggle dataset.  
consistent performance. Overall, the confidence intervals show that while SwT+Resnet50V2 is the best model, Resnet50V2 and the other models, with VGG16 being an obvious outlier, perform rather well. ConvNeXtTiny, with an accuracy of 69%, has a 27.8% difference compared to SwT+Resnet50V2. The confidence interval (56.92%, 81.08%) demonstrates considerable variability, indicating a moderate performance difference. On the other hand, EfficientNetV2B3 has the lowest accuracy at 34%, a significant disparity of 62.8% compared to SwT+Resnet50V2. In comparison to SwT+Resnet50V2, its confidence interval (21.92%, 46.08%) indicates significantly lower and more volatile performance. The confidence intervals indicate that Resnet50V2, VGG16, and EfficientNetV2B3 are outliers, showing varying levels of accuracy, with SwT+Resnet50V2 being the superior model overall.