## 5 Results  
This section presents the SETL_BMRI model's performance as well as important metrics for categorizing different kinds of brain tumors. The base models produced by VGG19 and AlexNet both have learning curves shown in Figures 8 and 9, respectively. The learning curves of the proposed model are presented in Figure 10, which shows the training accuracy rapidly improves with each epoch. The loss values notably show little variation, suggesting effective model convergence free from under- or overfitting.  
To confirm effectiveness, we tested the proposed ensemble design with independently modified pre-trained models. The quantitative results of the suggested SETL_BMRI ensemble model are shown in Table 3, along with the specific performance metrics (accuracy, precision, recall, and F1-score) of each model that has been optimized for brain tumor prediction. The models were refined using Kaggle brain MRI data. The ResNet50 model performed poorly, with an accuracy of 92.40%. DenseNet201 had 97.20% precision, 98.30% recall, 98.20% F1-score, and 97.85% accuracy, but AlexNet only had 94.05%. VGG19 per  
Figure 8: (**a**) Accuracy curve of AlexNet; (**b**) loss curve of AlexNet.  
Figure 9: (**a**) Accuracy curve of VGG19; (**b**) loss curve of VGG19.  
formed well with 97.02% accuracy, 97.30% recall, 95.70% precision, and 97.20 F1-Score. Table 3 clearly illustrates that the SETL_BMRI ensemble model outperformed individual models. SETL_BMRI outperformed all other models with 98.75% precision, 98.6% recall, 98.75% F1-Score, and 98.70% accuracy on the test dataset. Compared to other models, the proposed model showed a higher recall--important for medical image prediction models. The suggested model's improved generalization in comparison to other models is shown by the higher F1-score, which indicates superior predictive power.  
Comparing the confusion matrices of the SETL_BMRI with the pre-trained models in Figure 11 shows that the proposed model (SETL_BMRI) classified accurately and reduced misclassifications. The training and validation accuracy and loss curve of AlexNet are shown in Figure 8a,b, respectively. The suggested model outperformed the others in terms of performance, with ensemble methods working well to reduce classification mistakes and produce better results overall. The classification performance of the suggested SETL_BMRI model for each kind of tumor is summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Class** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline ResNet50 & Glioma & 1.00 & 0.81 & 0.90 & 92.40 \\  & Meningioma & 0.62 & 1.00 & 0.77 & \\  & No Tumor & 0.97 & 1.00 & 0.98 & \\  & Pituitary & 1.00 & 0.99 & 0.99 & \\ AlexNet & Glioma & 1.00 & 0.95 & 0.97 & 94.05 \\  & Meningioma & 0.72 & 0.96 & 0.83 & \\  & No Tumor & 0.98 & 1.00 & 0.99 & \\  & Pituitary & 0.98 & 0.86 & 0.92 & \\ DenseNet201 & Glioma & 1.00 & 0.98 & 0.99 & 97.85 \\  & Meningioma & 0.89 & 1.00 & 0.94 & \\  & No Tumor & 0.99 & 1.00 & 1.00 & \\  & Pituitary & 1.00 & 0.94 & 0.97 & \\ VGG19 & Glioma & 1.00 & 0.99 & 0.99 & 97.02 \\  & Meningioma & 0.83 & 1.00 & 0.91 & \\  & No Tumor & 1.00 & 1.00 & 1.00 & \\  & Pituitary & 1.00 & 0.90 & 0.95 & \\ Proposed & Glioma & 1.00 & 0.97 & 0.99 & 98.70 \\ SETL\_BMRI & Meningioma & 0.95 & 1.00 & 0.98 & \\  & No Tumor & 1.00 & 0.99 & 0.99 & \\  & Pituitary & 1.00 & 1.00 & 0.99 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class-wise comparison performance between the proposed SETL_BMRI model and other baseline deep learning models.  
Figure 10: **(a)** Accuracy curve of proposed model; **(b)** loss curve of the proposed model.