## 3 Proposed Methodology
### XG-Boost(XGB)
### Confusion Matrix

The proposed framework consists of CNN-CRF with RESNET-XG-Boost, and its results on the Three-classes datasets are shown in Table 2 using a confusion matrix. Table 3 shows the confusion matrix using an augmented dataset. The number of instances in the augmented dataset grows. In confusion matrices, non-white rows and columns represent network output classes and real classes, respectively. The correct classification is displayed along the diagonal. The larger number of non-white boxes is proportional to the number of images in the training or test set and vice versa. In this Table 2, the experiment is analyzed using a confusion matrix. It depicts a Three-classes classification analysis. In this experiment, more than 3064 original images are used, and 10-fold cross validation is used. The dataset was divided into 80:20 by this validation process. In 90 cases, training is provided, and in ten cases, testing is conducted. Because this process is repeated ten times, the average of the entire process is used in the results. All classes perform well in the table: Meningioma detection 100, Giloma detection 197, and pituitary approximately 300 correct prediction. Further, Table 3 depicts a Three-classes classification analysis with an augmented dataset. In this experiment, more than 4904 original images are used with 10-fold cross-validation. For the training, 90% of the dataset was used and kept 10% for the validation process. The experimental results showed all classes perform well in Table 3: Meningioma detection 2000, Glioma detection 1500, and pituitary approximately 1200 correct predictions. ### Detection of Tumor

Figure 3, shown above on page 6, depicts the detection of a tumor by displaying random instances from the Three classes shown in Table 3. It shows the results of CNN-CRF-RESNET experiments on two datasets, one in normal form and one in augmented form. In Table 4 and Figure 4, different classes are used in experiments on the datasets, and ten cross-validations are employed to improve the validation. The proposed method evaluates results based on accuracy, precision, recall, and F-score. It compared the original dataset to the augmented dataset. In comparison, Three-classes' accuracy improves from the normal dataset to the augmented dataset by approximately 1.2%, precision by 1.5%, recall by 1.3%, and F-score by 1.5%. This improvement demonstrates that increasing the instances improves performance metrics in two-class datasets, including accuracy, precision, recall, and F-score. It compared the original dataset to the augmented dataset. In comparison, two-class accuracy improves by 0.3% from the normal dataset to augmented dataset, precision by 0.4%, recall by 0.23%, and F-score by 0.3%. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Testing Approach Cross-Validation** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Three Classes & 10 & 99.56 & 98.56 & 98.12 & 98.45 \\ Three Classes Augmented & 10 & 99.45 & 98.78 & 98.12 & 98.56 \\ Two Classes & 10 & 99.12 & 97.34 & 98.12 & 97.89 \\ Two Classes Augmented & 10 & 99.89 & 98.12 & 98.45 & 98.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different datasets using the proposed approach. \begin{table}
\begin{tabular}{l l l l} \hline \hline Meningioma & 2000 & 51 & 10 \\ Glioma & 58 & 1500 & 0 \\ Pituitary & 36 & 0 & 1200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of three classes’ tumors with Augmentation. Similarly, two augmented classes achieved the highest F-score value. ### Comparison with Existing

A comparison with the findings of other studies that were conducted utilizing the same T1-CE MRI image collection is the one that is most suitable to be made with our results. Table 5 shows the comparison accuracy of our techniques with that of other eight published methods. Our approach exceeds the previous suggestions by achieving an accuracy of 0.9956 for tumor categorization.