## 3 Proposed Methodology
### XG-Boost(XGB)
In comparison, only accuracy is used because other parameters are not analyzed by the given research, and accuracy is an important parameter to analyze for the proposed model in our research. To validate our proposed technique and its impact on detection accuracy, we compared it to the work of other researchers who used SVM and Fisher Kernel machine learning based on detection accuracy ranging from 91% to 94%. The accuracy of CNN and CNN-based approaches ranges from 81% to 97%. Aside from that, the proposed method CRF-Resnet50 has a maximum accuracy of 99.56%. The proposed method is more effective than \(O(n^{2})\) when compared to existing approach complexities, but the proposed approach \(O(NlogN)\). Existing approaches use differential CNNs for classifier tuning, which reduces accuracy to \(O(n^{2})\). The proposed method improved segmentation while avoiding the use of a complex network. Segmentation is performed in existing approaches without regard to patch boundaries that overlap. The overlapping of information increases the error of tumor detection and localization. The proposed CNN segmentation approach maps in infinite nonlinear space, but conditional random field extracts condition-based finite space. This enhances detection accuracy by improving segmentation, feature mapping, and classification. Figure 4: Analysis of different performance metrics on Proposed approach. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Classification Method** & **Accuracy** \\ \hline [PERSON] [24] & SVM & 91.2 \\ [PERSON] [26] & Fisher Kernel & 94.7 \\ [PERSON] [20] & CNN & 84.1 \\ [PERSON] [19] & CNN & 81 \\ [PERSON] [19] & CNN and KELM & 93.6 \\ [PERSON] [27] & CNN & 96.1 \\ [PERSON] [28] & CNN and GA & 94.2 \\ [PERSON] [2] & Multi-Scale CNN & 97.3 \\ Proposed & CRF-Resnet50 & 99.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Three class results. ## 5 Conclusions

This study presents an accurate and automated brain tumor categorization method that requires little preprocessing. Deep Residual Learning is used to extract features from brain MRI images in the proposed system. The proposed method focuses on efficient segmentation by using a CNN-CRF-based approach to reduce overlapping edges. CRF provides condition-based decisions of segmented boundaries in this approach. We reduce the small tumor overlapping by other patches in non-overlapping borders. As a result, no overlapped patches improve cancer detection and detect efficient feature mapping by residual network and learning by an XG-Boost base ensemble classifier. The experiments employ two datasets: one for two classes (cancer and non-cancer) and the other for three classes (Meningioma, Glioma, Pituitary). We applied CNN-CRF-Resnet to both datasets (proposed approach), used 10-fold cross-validation, and analyzed the performance metrics. Accuracy, precision, recall, and F-Score are all important metrics, and we achieved the following values: accuracy 99.89%, precision 98.12%, recall 98.45%, and F-score 98.1% for the two classes. The following three-class experiments had improved accuracy by 99.56%, precision by 98.56%, recall by 98.12%, and F-score by 98.45%. There were improved results in the augmented approach by increasing the instances of all classes. The three classes all showed the same increase in performance. Compared to existing approaches, the two-class and three-class approaches significantly improve resources and computation. The proposed research aims to improve the overlap between patches and its effect on the mapping efficiency of features by residual network. In addition, the proposed work contributes learning and focus mechanisms for enhancing learning and features, respectively. Conceptualization, A.H.; Data Curation, A.H.; Formal Anal-ysis, A.H., A.H.O. ; Acquisition, A.H.; Methodology, A.H., A.H.O. ; Resources, A.H., A.H.O. ; Validation, A.H.O. ; Investigation, A.H.O. ; Visualization, A.H., A.H.O. ; Software, A.H.; Project Administration, A.H.O.