## 2 Related Work  
Recently, there has been a lot of work on brain tumor detection and classification [24; 25; 26; 27; 28; 29; 30; 31; 32]. For the detection and categorization of brain tumors, various techniques have been presented. These methods include traditional ML methods and DL methods. This section includes the investigation of existing brain tumor detection and classification approaches. [PERSON] et al. introduced an approach that integrates statistical features and neural network techniques [24] to detect and classify brain tumor MRI images. Region of inter est (ROI) is used in this method, defined as the tumor segment detected using any ROI segmentation technique. Moreover, 2D Discrete Wavelet Transform (DWT) and 2D Gabor filter techniques were used to determine features for the classifier. To create the feature set, they used many transform domain statistical features. For classification, a backpropagation neural network classifier was used. A Figshare dataset of 3064 slices of T1-weighted MRI images of three forms of brain tumors, meningioma, glioma, and pituitary, was used to evaluate the model. The authors achieved a maximum accuracy of 91.9%. A Deep Neural Network classifier, one of the DL frameworks, was used by [PERSON] et al. to classify a dataset of 66 brain MRIs into four categories: normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors [25]. The classifier was combined with the discrete wavelet transform (DWT), a powerful feature extraction approach, and principal component analysis (PCA), with promising results across all performance metrics. The authors achieved a maximum accuracy of 98.4% by combining DNN with DWT. [PERSON] et al. classified medical images using a combination of CNN features and SVM [26]. To analyze and validate their proposed approach, they used publicly available MRI images of brain tumors from Figshare that comprised three types of brain tumors. They extracted characteristics from MRI scans of the brain using the CNN classifier. For increased performance, a multiclass SVM was paired with CNN features. They also tested and evaluated an integrated system using a five-fold cross-validation technique. The proposed model surpassed the current techniques with respect to total classification accuracy by achieving a classification accuracy of 95.82%. When there is limited training data, the SVM classifier outperforms the softmax classifier for CNN feature extraction. They employed the CNN-SVM approach, which requires fewer computations and less memory than TL-based classification. For brain tumor identification, the authors of [27] presented a multi-level attention mechanism network (MANet). The suggested MANet incorporates both spatial and cross-channel attention, focusing on tumor region prioritization while also preserving cross-channel temporal relationships found in the Xception backbone's semantic feature sequence. The proposed method was tested using the Figshare and BraTS benchmark datasets. Experiments show that combining cross-channel and spatial attention blocks improves generalizations and results with improved performance with fewer model parameters. The suggested MANet outperforms various current models for tumor recognition, with a maximum accuracy of 96.51 percent on Figshare and 94.91 percent on BraTS'2018 datasets. In image detection and recognition challenges, CNN plays a significant role. To extract features automatically from brain images, CNN filters are convolved with the input image. Most research methodologies use CNN-based approaches for brain tumor detection and classification. [PERSON] et al. used capsule networks for brain tumor classification and investigated the overfitting problem of CapsNets using a real collection of MRI data [28]. CapsNets require far less training data, making them perfect for medical imaging datasets such as brain MRI scans. They built a visualization paradigm for CapsNet's output to better illustrate the learned features.