## 4 Experimental Results
### Classification Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**LMA Combination**} & \multicolumn{8}{c}{**Kaggle Dataset**} & \multicolumn{8}{c}{**FigShare Dataset**} \\ \cline{2-11}  & **A (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **RMSE (\%)** & **A (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **RMSE (\%)** \\ \hline In + De + E0 & 63.6 & 61.9 & 62.9 & 62.4 & 17.2 & 61.2 & 60.9 & 58.9 & 59.9 & 23.0 \\ \hline In + De + Da & 74.7 & 74.8 & 74.0 & 74.4 & 18.8 & 76.6 & 73.7 & 75.4 & 74.5 & 14.7 \\ \hline
**In + De + Mo** & **85.0** & **84.2** & **83.8** & **84.0** & **6.6** & **85.3** & **83.4** & **82.0** & **82.7** & **9.5** \\ \hline _In + E0 + Da_ & _82.1_ & _82.7_ & _82.8_ & _82.1_ & _14.8_ & _82.2_ & _82.1_ & _81.8_ & _81.9_ & _10.1_ \\ \hline In + E0 + Mo & 71.5 & 70.7 & 71.4 & 70.0 & 16.0 & 69.7 & 67.7 & 66.4 & 67.0 & 14.3 \\ \hline In + Da + Mo & 52.8 & 51.4 & 52.4 & 51.9 & 15.6 & 56.9 & 56.0 & 55.1 & 55.5 & 14.1 \\ \hline De + E0 + Da & 72.1 & 73.8 & 73.2 & 72.5 & 11.1 & 77.3 & 74.5 & 76.0 & 75.2 & 13.5 \\ \hline De + E0 + Mo & 75.9 & 75.9 & 74.6 & 76.7 & 18.5 & 79.7 & 78.6 & 77.7 & 78.1 & 16.4 \\ \hline De + Da + Mo & 81.5 & 81.4 & 80.3 & 82.3 & 12.8 & 80.0 & 81.1 & 81.3 & 81.2 & 12.4 \\ \hline E0 + Da + Mo & 70.1 & 70.3 & 71.2 & 69.7 & 23.0 & 68.2 & 65.6 & 67.5 & 66.5 & 15.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of pre-trained CNN models to find the best combination for the LMA module on the original dataset. **Bold** shows the best performance; _Italic_ shows the second best performance; underlined shows the third best performance. on the Kaggle dataset and 7.8% on the FigShare dataset. With essential augmentation, the model demonstrates a notable improvement, achieving an accuracy of 95.0% on the Kaggle dataset and 95.3% on the FigShare dataset. The precision, recall, and F1-scores also increased, reaching 94.6%, 94.4%, and 94.5% on the Kaggle dataset and 94.7%, 94.2%, and 94.4% on the FigShare dataset. RMSE values were reduced to 5.5% on the Kaggle dataset and 5.3% on the FigShare dataset, indicating enhanced prediction stability. The application of SMOTE yields the best results, with the model achieving an accuracy of 99.0% on the Kaggle dataset and 99.2% on the FigShare dataset. Precision, recall, and F1-scores are exceptionally high at 98.7%, 98.5%, and 98.6% on the Kaggle dataset and 98.8%, 98.6%, and 98.7% on the FigShare dataset. The RMSE values are minimized to 2.0% on the Kaggle dataset and 1.8% on the FigShare dataset, showcasing the significant impact of SMOTE in reducing prediction errors and enhancing the overall performance of the GATransformer model. Figure 10 displays confusion matrices for two datasets, Kaggle and FigShare, using the proposed model on SMOTE data. For the Kaggle dataset in Figure 10a, the model performs well with Glioma, achieving 1307 correct predictions and minimal misclassifications in the other classes. Meningioma has 1325 true positives, with a few misclassifications into the different courses. Pituitary shows a very high actual positive rate of 1440, and no tumor also performs well with 1574 correct predictions. The confusion matrix is dominated by true positives, which signifies [PERSON]'s well-performing model. For the FigShare dataset in Figure 10b, the model's performance is similarly good but with slight differences in misclassification. Glioma has 1411 correct predictions, meningioma has 693 correct predictions, and pituitary has 920 true positives, with a few misclassifications to other classes. The misclassifications for no tumor are pretty low. In both datasets, the model struggles slightly with predicting meningioma and pituitary classes accurately, as evidenced by their misclassifications. Still, overall, the model's accuracy is relatively high, as reflected in the overall trends of the confusion matrices. The color intensity in the heatmaps highlights these performance patterns, with darker shades representing higher numbers of predictions.