## 2 Literature Review  
Early detection and treatment for tumors in the brain are necessary for positive results. Detecting brain tumors using MRI and CT scans may be difficult because of higher complexity, more significant computation needs, and elevated false negative rates. An FT-ViT framework was developed for recognizing brain tumors via MRI scans [15]. Techniques include feature processing, feature engineering, patch processing, and fine-tuning. Based on trial results, the CE-MRI database-based model had 98.12% accuracy. However, this method requires a lot of computer power. The authors of [16] applied models trained with deep learning to develop an autonomous system for detecting brain tumors. This approach integrates the features of CNN with a computer-aided diagnostic tool for effective image processing. A transfer-learning-based modified classifier is applied to distinguish tumor kinds. This study used the public brain tumor dataset for verification. Framework sensitivity was 98.77%, accuracy 98.86%, and F-measure 98.71%. The system is lightweight and computationally viable, although overfitting constraints limit its usefulness. The authors of [17] developed a multiclass categorization strategy for predicting different kinds of brain tumors. The effectiveness of the randomly created TL framework was evaluated against pre-trained models like VGG16/19, ResNet50, Xception, and InceptionV3 using a 3264-image brain dataset. The deployment achieved 97.12% and 94.12% tumor categorization accuracy for binary and multiclass analysis. This accurate classification system helps radiologists make plausible conclusions. However, this causes many false negatives and positives. Transformer models are popular in computer vision due to their low inductive biases and processing requirements. A ViT-based DNN framework was developed for MRI-based brain tumor detection [18]. Pre-trained model-based and fine-tuned ViT skills diagnosed gliomas, pituitaries, and meningiomas 100% accurately. This technique scored 98.7% accuracy on a database of 3064 images of brain tumors. However, there are some problems in analyzing the methods of decision making. A comprehensive MRI brain tumor segmentation and classification approach was introduced, incorporating three-dimensional CNN and a Transformer framework [19]. This approach accurately retrieves both global and local spatial characteristics from photos. The retrieved features are transformed into tokens and transmitted to the Transformer encoder. The model developed surpasses modern models in terms of precision, as validated by the entire comparative assessment. However, this method needs additional training along with computational resources. A CNN-based technique was presented that can precisely segment and identify tumors in the brain from MRI scans [20]. This approach integrates pre-trained CNNs with adaptable optimization techniques to classify brain tumors. The method utilizes a pre-trained CNN as a classification algorithm and an adaptable dynamic sine-cosine fitness gray wolf optimizer for tuning its hyperparameters. Integrating appropriate fitness into the CNN boosts classification accuracy and error rate. However, this approach is ineffective for categorizing multiple kinds of cancers. U-Net architectures are widely used for medical image segmentation but face limitations when dealing with small tumor targets and fuzzy boundaries. To address these challenges, the study [21] proposed a modified U-Net architecture incorporating: attention mechanisms to guide the network to focus on relevant regions, improving segmentation accuracy, and multiscale feature fusion to enhance the network's ability to process information across multiple scales, improving segmentation efficacy for diverse tumor structures. Although not explicitly addressed, attention mechanisms could improve explainability by highlighting the regions of interest (e.g., tumor areas) that the model focuses on during segmentation.