## 1 Introduction  
Brain tumor detection heavily depends on medical imaging as well as classification to determine proper treatment planning and prognosis for patients. Combined with other computational methodologies, deep learning has driven significant innovations in analyzing medical images over time. Existing brain tumor detection techniques utilize manually engineered feature extraction and shallow learning algorithms that might not correctly identify complicated patterns in magnetic resonance imaging (MRI). This form of imaging is widely used in neuroimaging to help predict neurological disorders [1]. The latest studies focused on using MRI images to autonomously classify various kinds of tumors in the brain [2]. MRI scan characteristics were extracted using classic ML-based classifiers such as KNN, SVM, and RF [3, 4, 5]. Deep learning (DL) models like CNNs and DNNs fared higher in image recognition tests [6, 7]. Therefore, deep learning-based classifications are utilized to effectively identify brain tumors via MRI imaging. Recent advancements in deep learning have enhanced the interpretation of medical images, particularly the implementation of Transformer architectures. Initially designed for natural language processing, it extracts relevant features from high dimensional data while capturing spatial relationships. Despite its development, there are still serious problemsand restrictions regarding brain tumor diagnosis and classification. Conventional deep learning techniques and convolutional neural networks (CNNs) may face obstacles such as the interpretability of predictions, class imbalance, and insufficient information availability. The optimization of hyperparameters in deep learning models is a complex process that frequently necessitates a significant number of computational resources and expertise. This study proposes an innovative strategy for identifying and classifying brain tumors, overcoming existing drawbacks. These deep learning frameworks demonstrate inductive biases, complexity, extended training times, and significant computational capacities [8]. More enormous databases and data augmentation are required to ensure accurate classification results in the previously mentioned methods [9]. Transformers have been developed to tackle the above-mentioned problems in image processing. The adaptive Transformer architecture, including the visual Transformer, outperformed CNN classifiers and had fewer inductive biases than pre-trained techniques [10]. Transformer global patch-based learning reduces inductive biases and captures important MRI scan properties better than CNN models [11]. Transformer models' multi-head self-attention portion enhances feature engineering by targeting near-tumor regions, outperforming ordinary versions. However, training the Transformer model requires a lot of data and processing power. Transformer-based models, have enhanced medical image classification [12], including brain tumor diagnosis. These models are accurate but typically operate as black boxes, raising issues about explainability and trustworthiness in essential applications like healthcare. Medical decision making requires human monitoring for safety, reliability, and ethics. Medical AI has largely adopted the Human-in-the-Loop (HITL) [13; 14] strategy, which combines automated predictions with expert validation. This method works in radiology, pathology, and clinical diagnostics, where AI aids decision-making but the doctor makes the final call. In this study, we use self-attention techniques for explainability but acknowledge the need for human interaction in understanding AI-driven diagnoses and assuring clinical usability. This article aims to improve brain tumor classification models' accuracy, efficiency, and interpretability by incorporating Transformer structures and a self-attention graph attention network (GAT).