# Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images
## 2 Literature Review
Their primary goal was to create the ANN models in such a way that they can solve the convergence rate. They were successful in doing so, and after altering the accuracy rate, they achieved 95% accuracy for MKNN and 98% accuracy for MCPN. [PERSON] et al. [43] suggestion was to investigate the basic CNN model without making any modifications, merely working on CNN and adjusting the number of its various layers. They created seven alternative CNN designs in this way, each with a different number of layers, and concluded that the second architecture, achieved a training accuracy of 98.51% that consisted of two layers of convolution, activation, and max pooling, is the best of all of them. [PERSON] et al. [44], proposed a model that employed direct holistic 3D reconstruction of an image. The DenseNet was then used to extract features from the two-dimensional slices after first converting the three-dimensional holistic image into two-dimensional slices. A recurrent neural network model was created and tested to categorize the images. Two separate datasets, one private and the other public were used for testing. The DenseNet uses CNN as a convolutional auto-encoder for sequence learning. For the categorization and screening of tumors, DenseNet CNN and DenseNet short-term memory were also used. This method obtained an accuracy of 92.13% using DenseNet and LSTM. To categorize brain cancers, a model based on modified Capsule Neural Network (CapsNet) architecture was proposed by [PERSON] et al. [45]. The authors stated that there were two benefits of their model. It eliminated the need for accurate tumor annotation and CapsNet on the primary area while also establishing the connection with surrounding tissues. In comparison to earlier CapsNets and CNNs, their proposed model's classification accuracy increased to 90.89%. In [46], the authors presented a DL method for classifying BT severity. They used the BraTS2018 and BraTS2019 datasets in their experiments. They adjusted the DenseNet201 model using transfer learning to retrieve the features. Next, they used the modified genetic algorithm (MGA) and the Entropy-Kurtosis-Based High Feature Value (EKbHFV) to choose the best features. A nonredundant serial-based technique is used to perform the fusion, and the cubic SVM was then used to classify the results. They reached 95% accuracy. [PERSON] et al. [47], described a strategy that ensembled DL and ML to classify BT. For classification, they utilized the terms glioma, meningioma, and pituitary to refer to three distinct kinds of BT. They adjusted the GoogLeNet model using transfer learning to retrieve the deep features. With the aid of the SVM, KNN, and softmax, the retrieved features were then categorized. In order to investigate the BT, the authors of [48] compared the effectiveness of several DL models, including ResNet50, AlexNet, VGG16, and GoogleNet. The ResNet50 showed the highest performance that is 95.8% accuracy, and AlexNet showed the fastest processing, at about 1.2 s, which later fell to 8.3 msec when using GPU. Recently, [PERSON] et al. [49] proposed a ViT-based assembling model for BT classification, they used Figshare dataset for model evaluation and obtained 98.7% accuracy. In another approach, [PERSON] et al. [50] proposed a deep anchor attention learning method with the integration of ViT and showed its efficacy in the classification of overall survival in BT patients using MRI modality. [PERSON] et al. [51] presented a deep learning model for BT classification, their approach consisted of ten convolutions, 5 pooling, batch normalization, dropout, two fully connect, and a SoftMax layer. They compared the performance of their model with four state-of-the-art CNN models and ViT-based models obtained higher performance [51]. A ViT based model for an effective BT classification was proposed in [52] and obtained promising results. Similarly, some researchers used various versions of ViT for BT segmentation [53; 54; 55].