# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 4 Results and Discussions
### Comparing the Proposed Model with Various CNN and ViT-Based Models  
In this subsection, we compare the results of our model with various types of CNNs and ViT models to evaluate the model effectiveness. In consideration, the proposed model  
\begin{table}
\begin{tabular}{c c} \hline \hline
**References** & **Accuracy** \\ \hline
**[PERSON] et al.**[72] & 97.64\% \\
**[PERSON] et al.**[28] & 94.68\% \\
**[PERSON] et al.**[69] & 83.33\% \\
**[PERSON] et al.**[76] & 86.56\% \\
**[PERSON] et al.**[67] & 91.28\% \\
**[PERSON] and [PERSON]**[71] & 97.10\% \\
**[PERSON] and [PERSON]**[75] & 96.95\% \\
**[PERSON] et al.**[77] & 90.27\% \\
**[PERSON] et al.**[78] & 93.68\% \\
**[PERSON] et al.**[74] & 94.80\% \\
**[PERSON] and [PERSON]**[79] & 95.82\% \\
**[PERSON] et al.**[66] & 95.23\% \\
**[PERSON] et al.**[73]. & 98.04\% \\
**Proposed Model** & 99.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed model with the state-of-the-art methods using the FIGSHARE dataset.  
Figure 13: Confusion matrix of the proposed model using FIGSHARE datasets, where M represents Meningiomas, G Gliomas, and P Pituitary tumors.  
is compared with Solo AlexNet, Inception V3, VGG19, ResNet50, and ViT using BraTS 2018 and FIGSHARE datasets as presented in Table 5. The proposed model obtains higher accuracy compared to other models using BraTS 2018 and FIGSHARE. In comparison, the proposed model is efficient in terms of learning parameters of 22.5 million. Thus, the proposed model is an efficient and effective solution for brain tumor classification as proven by these experimental results.  
The visual result of the proposed model is demonstrated in Figure 14, where the GT indicates the ground truth and MP represents model prediction. It can be seen that the proposed model is confused in the prediction of two samples. Our model misclassifies the last image of the first row and the second image of the second row as shown in Figure 14 with red color. We believe that this misprediction is due to the visual similarity with other classes.  
Figure 14: The visualized result of the proposed model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Reference** & **Method** & **Parameters (M)** & **Accuracy** \\ \hline \multirow{8}{*}{BraTS 2018} & \multirow{8}{*}{[PERSON] et al. [51]} & AlexNet & 60 & 92.20 \\ \cline{3-4}  & & Inception V3 & 23.9 & 94.66 \\ \cline{3-4}  & & VGG19 & 143.7 & 93.26 \\ \cline{3-4}  & & ResNet50 & 25.6 & 91.78 \\ \cline{3-4}  & & ViT & 93.48 \\ \cline{3-4}  & & CNN with Ten Conv layers & – & 94.72 \\ \cline{2-4}  & **The proposed model** & **TECNN** & **96.75** \\ \hline \multirow{8}{*}{FIGSHARE} & ViT-B/16 (224 \(\times\) 224) & 86 & 97.06 \\ \cline{3-4}  & & ViT-B/32 (224 \(\times\) 224) & 86 & 96.25 \\ \cline{3-4}  & & ViT-L/16 (224 \(\times\) 224) & 307 & 96.74 \\ \cline{3-4}  & & ViT-L/32 (224 \(\times\) 224) & 307 & 96.01 \\ \cline{3-4}  & & ViT-B/16 (384 \(\times\) 384) & 86 & 97.72 \\ \cline{3-4}  & & ViT-B/32 (384 \(\times\) 384) & 86 & 97.87 \\ \cline{3-4}  & & ViT-L/16 (384 \(\times\) 384) & 307 & 97.55 \\ \cline{3-4}  & & ViT-L/32 (384 \(\times\) 384) & 307 & 98.21 \\ \cline{3-4}  & & Ensemble of ViTs & – & 98.70 \\ \cline{2-4}  & **The proposed model** & **TECNN** & 22.5 & **99.10** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing our model with various versions of solo CNN and ViT-based models for BT classification.