## 3 Proposed Research Methodology
### Data Augmentation  
Data augmentation is used to increase the size of a dataset which helps the model's generalization capacity by lowering the danger of overfitting and being especially effective when the dataset is scarce. Different transformations can be used to improve image collection. These include flipping, rotation, and translation [57]. These transformations add more samples to the collection by altering the source images. Moving an image in a different direction (horizontally or vertically) helps produce new pieces by repositioning the object within the image [58]. In our case, we applied the changes using the flipping, rotation, and translation techniques as shown in Figure 6. The most popular methods include. * Flipping: produces a mirror image of the original. * Rotation: shifting an image at an angle concerning its central pixel. * Translation: involves shifting the image along the X or Y direction or both. The dataset of brain tumors utilized in the study includes 1908 images, as indicated in Table 2. The datasets are split into three categories: testing (220 images), validation (150 images), and training (1908 images). To create three times as many new photos, this dataset was expanded. Figure 7 and Algorithm 1 provide a detailed visual depiction of the architecture that our model proposes for the classification of brain tumors. Figure 6: Brain tumor image after applying data augmentation approach. Figure 7: Proposed SETL_BMRI for multiclassification of brain tumor. ```
Input: Images Dataset (IDS) Output: Evaluation metrics
1. Load IDS
2. Preprocessing IDS
3. Applying Data Augmentation (DA)
4. N \(\leftarrow\) Number of images in IDS
5. I \(\leftarrow\) 1
6. while I \(\leq\) N
1. Read images (IDS(I))
2. Flip images
3. Rotate images 90 degrees
4. Translation Images
5. I = I + 1
7. End
8. split dataset ()
9. load proposed learning model (LM)
10. for each epoch in epoch number
1. \(\text{y}^{*}\) = model (features extract)
2. loss = cross entropy (y, y')
3. \(\text{ptimize (loss, accuracy)}\)
11. return ()
12. print model evaluation
13. print accuracy curve, precision, recall, and F1-score
14. Classify brain tumors in MRI images
```  
**Algorithm 1** Proposed Methodology  
The proposed model has been used by state-of-the-art CNN architectures AlexNet and VGG19 along with a data augmentation approach. The selection of these models stems from their remarkable performance in medical imaging tasks and their proven success in real-time applications. Notably, the pre-trained weights of both models were selected from the ImageNet dataset. The goal of reducing detection errors while simultaneously improving accuracy and resilience is the reason for using a deep ensemble model as opposed to a single CNN model. This model uses the meta-learner that concatenated prediction vectors from all base learners as features. For multi-class classification at the second level, a brand-new fully connected neural architecture known as the meta-learner is used. The basis models are held frozen, while the meta-learner is taught using the training set. The pre-trained CNN and the suggested SETL_BMRI model for brain tumor prediction are covered in detail in the next sections. These models have performed well on the ImageNet dataset and have seen significant advances. To assess the efficacy of the model, it is recommended to split the dataset into distinct training, testing, and validation sets. Additionally, it is advisable to restrain the upper (classification) layers of the pre-trained VGG19 and AlexNet models before their integration. This precautionary measure ensures that the established layers \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Number of Images** & **After Augmentation** \\ \hline MRI images & 1908 & 5724 \\ \hline Glioma Brain Tumor & 495 & 1485 \\ \hline Meningioma Brain Tumor & 488 & 1464 \\ \hline Pituitary & 395 & 1185 \\ \hline No Tumor & 530 & 1590 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MRI images dataset before and after augmentation. remain unchanged upon loading.