## 4 Results and Discussion  
The CNN + XGBoost model performance in the multiple classification of brain tumors will be presented in this section. The contribution of model decision explanations to the classification process by adding the Grad-CAM method will be discussed. Additionally, the efficacy of the model will be assessed by comparing the outcomes with other machine learning methods, pre-trained models and state-of-the-art methods. Additionally, the efficacy of the model is assessed by comparing the outcomes with other machine learning methods, pre-trained models and state-of-the-art methods, all tested under the same experimental conditions. All models were trained and evaluated using the same dataset, feature sets, preprocessing steps, and data split ratios to ensure consistency and fairness in comparison. Four different brain tumors were classified by the CNN + XGBoost model as pituitary, meningioma, glioma, and notumor. The confusion matrix for classification results is displayed in Figure 4. The matrix shows that the model generally predicts classes with high accuracy rates. The model incorrectly identified 1 out of 300 images as meningioma in the glioma class, whereas it correctly identified 2 out of 306 images as gliomas in the meningioma class. Such a result depends on the classification ability of the model as well as the medical characteristics of the tumors. Although glioma and meningioma tumors originate from different cell types, in some cases their morphological characteristics may be similar. Large and invasive meningioma can be confused with gliomas. Such meningioma can spread to deeper areas of the brain and show similar characteristics to gliomas as they come into contact with the meninges. Therefore, it becomes difficult to distinguish them from gliomas. While gliomas have a rapidly growing irregular structure, meningioma grow slowly and affect a limited area. However, some can grow rapidly and show similar invasive characteristics to gliomas. As a result, it becomes difficult to distinguish such tumors for the model. Every image in the other two classes was appropriately classified. A model with a very low rate of misclassification and a high rate of class separation was produced when a general analysis is performed on the precision, recall, and F1 score values for each class shown in Table 4.  
Figure 4: Confusion matrix of proposed model. Figure 5 shows samples of each class obtained as a result of the classification of MRI scans by the model. Labels expressed as \"True\" indicate the actual class of the image, and the one above shows the class label predicted by the model. Green labels show correctly classified samples, and red labels indicate incorrectly classified samples. Three samples whose classes were incorrectly predicted by the model and some correctly classified samples are visualized. The model generally classified the images correctly. Misclassifications may be due to the similar structural features of tumors. In Figure 6a, each column shows three original MRI images and the class labels predicted by the model for the classes glioma, meningioma, notumor and pituitary, respectively. In (b), the regions that the model takes into consideration when assigning this class label are visualized with Grad-CAM. In the generated heat maps, red and yellow colors indicate the most important regions, while blue and green colors indicate less important regions. In the glioma image, the tumor is usually located in the deep cerebral regions, and this region is clearly highlighted in red on the Grad-CAM maps. This shows that the model can correctly identify the tumor area. In the meningioma image, the model correctly detected the location of the tumor near the dura mater and the activation maps generally showed high focus around the lesion. In the notumor image, the model focused on a wider area and could not highlight a distinct region because there was no tumor.