# A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network
## 5 Discussion  
The final architecture was formed by applying multiple parameters, including the changes in the dropout layers and optimization using SGD, to the VGG-16 configuration. Due to the multiple models and datasets used, it is difficult to compare the findings to other approaches reported at the state-of-the-art. However, we did manage to compare existing methods for detecting and classifying brain tumors in Table 5. We have applied the same dataset to our proposed model which is used in the following studies. The approach taken was segmentation-free. The authors of [54] adopted a GA to identify the network architecture, although the prediction results did not implement the best one. In [53], the author incorporated the coarse tumor boundary as an additional input to assist the network to produce better outcomes. Furthermore, the authors of [52] applied only two convolutional layers, each having 64 kernels. Therefore, the proposed method predicts the best result when compared to \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Tumors** & **TP** & **TN** & **FP** & **FN** & **Precision** & **Recall** & **Specificity** & **Accuracy** & **F1-Score** \\ \hline Glioma & 529 & 1510 & 3 & 18 & 0.994 & 0.967 & 0.988 & 98.98 & 0.98 \\ Meningi-oma & 511 & 1532 & 17 & 1 & 0.967 & **0.998** & **0.999** & 99.13 & 0.98 \\ No Tumor & 455 & 1601 & 2 & 2 & 0.996 & 0.996 & 0.998 & 99.81 & 0.99 \\ Pituitary & 543 & 1516 & 0 & 1 & **1.00** & **0.998** & **0.999** & 99.95 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis of the proposed model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Serial** & **Author** & **Model Used** & **Dataset Used** & **Model Accuracy** & **Our Model Accuracy** \\ \hline
1 & [PERSON] et al. [52] & Fully Connected Network (FCN), CNN & 3064 T1-weighted contrast-enhanced images with three kinds of brain tumor [44]. & 91.43\% & 96.4\% \\
2 & [PERSON] et al. & incorporated with & 3064 T1-weighted contrast-enhanced images with three kinds of brain tumor [44]. & 90.89\% & 96.4\% \\
3 & [PERSON] et al. & Genetic Algorithm (GA) & 3064 T1-weighted contrast-enhanced images with three kinds of brain tumor, combined with data from other sources & 94.2\% & 95.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed structure with existing studies. other relevant past research on multi-class classification types, demonstrating the proposed system's reliability. Additionally, in Table 6, after comparing the proposed model with existing models, it is clear that our model provides the highest accuracy, while popular architectures, such as EfficientNet and ResNet remain behind. Even though our model is not pre-trained, it achieved 99.5% accuracy, whereas the pre-trained EfficientNet and ResNet models generated between 87-99.3% accuracy, which proves the validity of the modified VGG architecture. As the proposed model is a modified version of the VGG-16 architecture, after comparing the precision, recall, F1-score and accuracy, it can be seen that the modified VGGNet is performing better than the base VGG-16 model in Table 7. The VGG-16 model has been trained using the same hyperparameters we have used in the modified VGGNet. We used learning rate = 0.01, momentum = 0.0 as hyperparameters for VGG-16 and modified VGGNet experiments and for other CNN architectures (ResNet, EfficientNet), we used learning rate = 0.001 and momentum = 0.9. Even though the total parameters of our model are much higher than the existing models, the evident advantage of having a large number of parameters is the ability to describe far more intricate functions than those with fewer parameters. Deep Neural Networks deal with a large number of training and testing parameters. The ability of neural networks to fit different sorts of information as the number of parameters rises is what makes them so powerful. Our model is versatile enough to describe the necessary mapping because it has numerous parameters.