## 3 Proposed Methodology
### Datasets
#### 3.1.4 Smote  
The over-sampling method, which involves augmenting the minority class by generating synthetic images from the original minority class images, influenced traditional SMOTE techniques [28] and standard machine learning data augmentation methods like rotation and mirroring. Synthetic examples are generated by operating directly in \"image space\" rather than \"feature space\". The minority cases are over-sampled when Gaussian noise is randomly introduced to \"n\" instances selected from minority cases. The magnitude \"n\" is ascertained according to the extent to which over-sampling is necessary.  
In [25], which consists of 1321 glioma, 1339 meningioma, 1457 pituitary, and 1595 no tumor cases, leave-one-out cross-validation requires balancing the training set by creating or removing synthetic cases. When a glioma case is left out, 19 synthetic meningioma cases, 137 synthetic pituitary cases, and 275 synthetic no-tumor cases must be removed to balance the dataset at 1320 cases per class. Similarly, if a meningioma case is left out, 17 synthetic glioma cases, 136 synthetic pituitary cases, and 274 synthetic no-tumor cases are removed to achieve the same balance. When a pituitary case is excluded, 18 synthetic glioma cases, 18 synthetic meningioma cases, and 18 synthetic meningioma cases, and 274 synthetic no-tumor cases are removed. Finally, if a no-tumor case is left out, 18 synthetic glioma cases, 18 synthetic meningioma cases, and 136 synthetic pituitary cases are removed to balance the dataset at 1321 cases per class. For this work's experiments, 1320 cases per class are kept on this dataset.  
In [26], which contains 1426 glioma, 708 meningioma, and 930 pituitary cases, leave-one-out cross-validation similarly requires balancing the training set. If a glioma case is left out, 717 synthetic meningioma cases and 222 synthetic pituitary cases must be removed to balance the dataset at 708 cases per class. When a meningioma case is excluded, 719 synthetic glioma cases and 223 synthetic pituitary cases are cleared. Finally, if a pituitary case is left out, 718 synthetic glioma cases and 221 synthetic meningioma cases are removed to achieve balance. This process ensures that, in each cross-validation iteration, the training set remains balanced across all classes after leaving one instance out. For this work's experiments, 708 cases per class are kept on this dataset.  
Synthetic images are produced by the subsequent method: Select \(m\) minority examples at random. Compute the standard deviation of the \(m\) images. Gaussian noise with a mean of \(R\) and \(S\) of the standard deviation of the \(m\) selected minority-case images is applied to each of the m photos to produce \(m\) additional synthetic minority images. Our approach presently utilizes \(R=0\) and \(S=1\) configurations.  
Figure 5a presents an example of an original tumor image, whereas Figure 5b displays the matching synthetic image. Figure 5c illustrates the binary difference map between the original and artificial images. A pixel value 0 in the difference map indicates that no change occurred after adding. A pixel value of 1 indicates that the pixel's intensity in the synthetic image changed due to the added Gaussian noise.