# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 4 Experimental Results
The performance of the ensemble model was compared, and the results using the public dataset are tabulated in Table 3.  
Furthermore, the confusion matrix (CM) and receiver operating characteristics (ROC), powerful tools for evaluating and comparing classification models, were used to perform another quantitative evaluation of the proposed methods. The first row of Figure 4 shows the individual model's CM, which is extremely useful for determining which classes were misclassified the most. Moreover, other metrics (e.g., precision, F1-score, and recall) may be calculated from a given matrix. The second row of Figure 4 shows the ROC which provides robustness analysis using graphical illustrations for a given class. Technically, intermediate curve points are constructed by changing the decision threshold (i.e., the control parameter) to classify instances into positive or negative classes. Various trade-offs between Tp and Fn rates are generated by this threshold, and the curve is constructed. Additionally, broken black (diagonal) represents the random guess, for which ROCs falling below this line indicate poor model performance in identifying such classes. ROCs are thus valuable assessment tools showing how well an ML classifier distinguishes between different classes. The ROCs and CMs of the proposed ensemble method tested using the first (public) dataset are shown in the Figure 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Evaluation Metrics, (\%)**} \\ \hline
**Method** & **Ac** & **Se** & **Sp** \\ \hline Hand-crafted Features (Hcf) & 84.05 & 82.99 & 94.11 \\ CNN-derived Features (CnF) & 79.20 & 78.52 & 92.21 \\ ViT-derived Features (ViF) & 92.77 & 92.54 & 97.50 \\ \hline CnF+Hcf & 93.74 & 93.21 & 97.88 \\ CnF+ViF & 88.86 & 88.08 & 96.01 \\ ViF+Hcf & 94.66 & 94.24 & 98.15 \\ \hline Proposed (public dataset) & 99.19 & 99.52 & 99.53 \\ \hline Proposed (local dataset) & 99.38 & 99.46 & 99.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall model’s accuracy and the associated ablation studies using various metrics. Ac: accuracy; Sn: sensitivity; Sp: specificity; CNN: convolutional neural network; and ViT: vision transformer. Figure 4: Model performance demonstrated using confusion matrices (first row) and ROC curves (second row) based on using an individual set of features texture (**a**,**d**), CNN-derived (**b**,**e**), and ViT-derived (**c**,**f**), respectively. To demonstrate the effectiveness of the proposed architecture, the trained model on the public dataset was used to classify brain images from the local dataset. The accuracy is reported in the last row of Table 3, and both the confusion matrix and ROC curves are shown in Figure 6. The results obtained on the second (local) dataset demonstrate how powerful the proposed model is in generalizing to a different dataset. In total, the quantitative results and scores of the ROC and CMs document the higher performance of the proposed model in correctly classifying brain MRIs. Finally, to emphasize the benefits of the proposed architecture, we compared the accuracy of our proposed model with its competitive recent literature models that partially utilized similar datasets or combined datasets. Some comparative models use the Figshare dataset compared to our study, which uses three datasets from public sources, including Figshare. We also compared against similar approaches that have adopted an aggregation of different datasets to ensure that our methodology's efficacy and generalizability are fairly assessed. To strengthen the argument for our framework and demonstrate its advantages without being unfair to other authors, we deliberately avoided reimplementing the approaches or methodologies of competitive state-of-the-art (SOTA) models. Instead, we focused on comparing the accuracy of our proposed model with recent literature models that utilized similar datasets.