###### Abstract
The models can be categorized into three groups: deep learning methods, machine learning techniques, and multi-atlas registration (MAS) algorithms. We provide a definition of MAS as the process of assigning labels and merging multiple standard brain atlases into a novel way of visual representation. The performance of these MAS algorithms has been insufficient for applications that demand high speed. Handcrafted (or predefined) features serve as the essential method for categorizing machine learning systems [16]. Before training a model to differentiate between tumors and normal tissue, significant data are obtained from the input image using a range of feature extraction approaches. Classifiers commonly used in machine learning (ML) techniques include random forest [17], support vector machines (SVMs) [18], and fuzzy clustering [19]. The process of extracting attributes, edge information, and other essential data using designed methodologies and attribute extraction algorithms requires a significant amount of time. Moreover, when there is ambiguity or lack of clarity in distinguishing between healthy tissues and malignancies, these techniques demonstrate poor performance. Significant features are automatically extracted by deep learning algorithms. These strategies have produced significant outcomes in many different industries. Several research studies have been conducted to determine the exact position of the tumor. One significant study by [20] employed segmentation techniques for this purpose. Several scholars have adjusted manually the hyperparameters of the model. In the study conducted by [21], a cancer diagnostic accuracy of 90% was attained using a combination of trial and error and manual correction. Nevertheless, conventional methods failed to provide optimal precision and results. Meta-heuristic algorithms can enhance classification accuracy in both traditional and deep learning methods. Moreover, vision transformers, which are a distinct type of architecture compared to CNNs, have demonstrated positive results in other domains, such as diseases related to brain tumors [22; 23; 24]. To effectively represent complicated visual patterns, vision transformers apply an attention mechanism to capture connections and long-range mutual dependence among image patches. Computer vision applications have recently expressed interest in this architecture because of its outstanding efficiency in natural language processing applications [25; 26]. Vision transformers showed the ability to gather both global and local image information, providing more comprehensive and accurate analysis in brain tumor classification. Due to their ability to produce significant representations directly from unprocessed data, they are a promising candidate for analyzing medical images, perhaps leading to advancements in the detection of brain tumors [27]. Further exploration and evaluation of vision transformers' potential in brain tumor classification systems could significantly enhance their accuracy and effectiveness. Traditional machine learning approaches for brain tumor identification typically involve two steps: the lesion segmentation and feature extraction processes. Segmentation, a time-consuming process, has imperfections that arise from variations in image quality, and these imperfections lead to errors in classification. While feature extraction has its use in extracting usable information from images, some procedures may need the use of specific knowledge in a certain image processing field. Moreover, reliance only on the qualitative characteristics of the tumors during their classification can be problematic because the various forms of tumors look very similar to each other; such an approach was used in [8; 9; 10; 13]. These limitations explain the necessity for high-level systems to be able to identify discriminative features autonomously and tackle the complexity of brain tumor detection.