# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 2 Literature Review  
In recent years, researchers have previously examined various medical image diagnosis techniques based on ML and DL methods [30; 31; 32; 33; 34]. The discipline of medical image analysis, particularly the field of disease identification, has benefited immensely from the introduction of new technologies involving deep learning and artificial intelligence. There have been numerous studies on CNN-based BT multiclassification and tumor detection. There are several ways to approach the research in the literature. For instance, some researchers have used CNN models that they have independently created to do BT classification, while other researchers have achieved the same goal by using transfer learning strategies. [PERSON] et al. [35], recommended using the pre-trained pipeline named the ResNet-34 CNN model when utilizing MRI data to identify BTs. In addition, their proposed model obtained optimal performance in terms of accuracy using a publicly available dataset. Furthermore, their proposed model is capable of accurately classifying BT such as meningioma, glioma, and pituitary tumors. Another approach presented by [PERSON] et al. [36], used different pre-trained architectures (namely AlexNet, GoogleNet, and VGG16) with some fine-tuning strategies to classify the BT into benign or malignant classes. They used a publicly available MRI dataset of 233 patients and achieved accuracy of 98.69%. A later approach was introduced by [PERSON] et al. [37], applied a transfer learning technique based on Deep Learning (DL) which categorizes the BTs into benign and malignant. Their experimental work is focused on the pre-trained CNN-based architectures known as ResNet101, ResNet50, GoogleNet, AlexNet, and SqueezeNet. The AlexNet gives a comparatively promising performance as compared to other models. Another approach  
Figure 3: Our proposed framework for brain tumor classification. Figure 2: Shows the Vision Transformer architecture proposed by [PERSON] et al. [28]. devised by [PERSON] et al. [38], utilized MRIs dataset with data augmentation technique to enhance the performance of the proposed method for accurate classification of BT. They employed an edge detection method on region of interest (ROI) that is selected before extracting the data from an MRI image using a typical CNN model. In addition, their proposed model obtained high performance in terms of evaluation matrices such as precision. BT classification using a CNN-based Computer-Assisted Diagnosis (CAD) method was developed by [PERSON] et al. [39]. Three different datasets were employed in trials with the 18-weighted layered CNN model, and obtained 90.35% of tumor rating accuracy and 94.74% classification accuracy. In order to predict tumor grade without the aid of expert annotations of regions of interest, [PERSON] et al. [40] employed CNN in 2018. They compared two methods of prediction: one used data from the entire brain and the other one used data from an artificially identified tumor location. They obtained 89.5% accuracy with the grade prediction from the complete brain image and also obtained 92.98% accuracy with the grade prediction from the tumor ROI. The two models are completely connected and convolution neural networks are proposed by [PERSON] et al. [41] who also conduct classification using a dataset with three classes divided into three separate planes. The authors simply assessed the model by just selecting the axial plane for performance accuracy in order to prevent any confusion for the model between the three different planes. Their experimental results showed that the CNN is optimally better, with a 91.43% accuracy rate. [PERSON] et al. [42], implemented a CNN-based model for diagnosing brain disorders. They achieved that by resolving the convergence time period constraints of ANNs when employing MRI. To do this, they implemented two modified models of the Counter Propagation Neural model (CPN) and the Kohonen Neural Network (KNN), referred to as MCPN and MKNN, respectively.