## 2 Related Work  
There are many studies that have performed binary and multiple classification of brain tumors using small or large datasets with machine learning and deep learning methods [15]. It is of great importance to perform brain tumor diagnosis automatically, quickly and accurately. Therefore, many methods have been proposed to increase accuracy and performance. To classify brain tumors, a deep convolutional neural network architecture was proposed by [PERSON] et al. [16]. The quality of the used MRI images was enhanced by a three-step preprocessing technique, which produced a 97.72% classification accuracy for four distinct tumor types. [PERSON] et al. [17] developed a CNN model that used grayscale MRI input and expanded the dataset to fourteen times its original size. In test and validation data, the model's accuracy in predicting the tumor or notumor was 88.25% and 96.7%, respectively. However, they only tested their model on a smaller dataset and performed a binary classification. A deep learning model for classification on datasets with different dimensions and modalities was presented by [PERSON] et al. [18]. This model uses an end-to-end cumulative learning strategy (CLS) and multi-weighted new loss (MWNL) function to reduce the effect of imbalance in the data and the overfitting problem with regularization functions such as DropOut and DropBlock. A modified RandAugment is proposed for the limited data problem and the classification success is increased with KNN. The proposed model outperforms other methods by reaching 99.7% accuracy when applied to small and imbalanced datasets. [PERSON] et al. [19] proposed a model performing feature extraction with pre-trained GoogleNet and classification with SVM and KNN. Compared with other models, this model had the highest value accuracy for classifying a 3-class brain tumor using the CE-MRI Figshare repository and the Harvard Medical Archives. [PERSON] et al. [20] proposed a hybrid model that classifies brain tumors as either benign or malignant by integrating CNN and SVM methods. In the study performed using a BRATS dataset, images were normalized, important features were selected utilizing a maximally stable extremal regions method and segmentation was performed. Following that, the CNN + SVM hybrid model classified the images with 98.4% accuracy. Recently, XAI methods have become increasingly used to explain model decisions due to the lack of interpretability and understandability of the outputs obtained from DL and ML approaches [21]. [PERSON] et al. [22] proposed a dual-input CNN approach integrating SHAP and LIME methods to improve the model's interpretability and classify brain tumors using MRI images. Utilizing a dual-input CNN model, robustness against noise and metal artifacts in image quality was ensured and brain tumors were multi-classified with a 94.64% accuracy rate by managing with low-quality images. Ten distinct transfer learning models were compared with regard to brain tumor classification performance by [PERSON] et al. [23]. The top-performing models were identified as EfficientNetB0, DenseNet121, and Xception, while the models' outputs were interpreted using Grad-CAM++ and Grad-CAM. It was shown that transfer learning models provide a more efficient training process with the available data and provide high accuracy classification success. A three-stage method for brain tumor segmentation and classification was proposed by [PERSON] et al. [24]. The DeepLabV3+ model was used for segmentation in the first phase. Images were classed in the second phase after characteristics were retrieved using the Darknet53 and MobileNetV2 models and fed into the SVM model. In both the first and second stages, hyperparameters were tuned with Bayesian optimization. In the last stage, the decisions of the model were explained with XAI techniques. A 98% accuracy rate was achieved for segmentation and 97% for classification.