# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 1 Introduction  
Brain tumors (BTs) are considered one of the leading types of cancer throughout the globe [1, 2]. It is essential to detect and localize the BT in the initial stage to save human lives. There are two types of BT that are Benign and Cancerous (malignant) tumors. The benign BT is a low-grade tumor, where a patient has a high chance of survival. The malignant BT is a high-grade tumor, which can quickly spread to other regions of the body and it is difficult to treat [3, 4]. Therefore, selecting a precise treatment strategy requires early detection of the correct type and grade of malignancy. An efficient method to identify BT is to look at the patient's Magnetic Resonance Imaging (MRI) scans of their brain [5]. MRI is one of the most common methods that is used to scan a brain under the influence of a very strong magnetic field. MRI uses radiofrequency signals to excite the target tissue so that a picture of its interior can bemade. It is effective because it shows soft tissue well and does not expose the patient to any ionizing radiation. Hence, the MRI is commonly used for finding problems in the brain [6; 7; 8]. Due to significant variability and inherent MRI data properties, i.e., variability in tumor shapes, and sizes, analyzing the affected region, and finding uncertainty in the segmented areas, BT is a difficult task to perform. Tumor classification is the most significant problem in BT image analysis. Examples of applications include tumor volume estimation, tissue classification, blood cell delineation, tumor localization, atlas matching, surgical planning, and image registration. The morphological and precise tumor quantification is a critical task for observing oncologic therapy. Although substantial work has been carried out in this field, physicians are still dependent on manual tumor detection, which is time-consuming and tedious work. The emergence of new technologies, particularly ML and deep learning, has a significant influence in the healthcare sector. As a results, machines have been used as a second opinion for early disease detection [9; 10]. Since it has given medical departments including medical imaging an essential support tool to interpret MRI images and support the radiologist's choice, a variety of machine and deep learning algorithms are used for segmentation and classification. There have been many Computer-Aided Diagnostic (CAD) systems used by radiologists in recent years that are based on ML [9]. To determine if a tumor is normal or pathological, the earliest models for tumor prediction relied on feature representations such as wavelet features and ML algorithms. The subsequent class of models use various sets, i.e., boundary, texture, and form of features simultaneously, which are high dimensional characteristics that are reduced to generate features maps. These feature maps are trained using an ensemble of classifiers including Artificial Neural Network (ANN), K-Nearest Neighbor (k-NN), and Support Vector Machine (SVM) [11]. [PERSON] et al. exploited intensity, neighborhood, and wavelet characteristics of brain MRI images as features. These features are merged and fed to random forest classifiers to forecast varying tumor levels [12]. In texture-based characteristics, an SVM classifier, and deep neural networks are used to classify BT [13]. Convolutional Neural Networks (CNN) quickly supplant traditional machine learning approaches in numerous application domains [14; 15; 16; 17; 18; 19; 20]. The CNN contains four basic layers such as a convolutional layer, a pooling layer, a fully connected layer, and an output layer. The convolutional layer is responsible for optimum feature extraction from the given input images by using different kinds of filters including edges, colors, shapes, etc. Afterward, the extracted features maps are subjected to a pooling layer for dimensionality reduction [21], there are three different types of pooling operation such as maximum pooling, minimum pooling, and average pooling.