Brain Tumor holds a significant holds in human health, classified into three primary types: glioma, meningioma, and pituitary tumors. Early detection and accurate classification are vital for effective diagnosis and lowering healthcare costs. In PBvit we presents a novel brain tumor detection framework, the Patch Base Vision Transformer (PBVit). PBVit adopts a patch-based approach where input tumor images are divided into fixed-size patches, with each patch treated as a token. These image patches are linearly projected into lower-dimensional token embeddings, and positional encodings are added to help the model understand spatial relationships within the image. PBVit enhances the detection of intricate patterns and anomalies in brain scans, improving diagnostic accuracy. We trained PBVit using the Figshare brain tumor dataset and observed notable performance improvements compared to traditional CNN-based models. The PBVit reached an accuracy of 95.8%, a precision of 95.3%, a recall of 93.2%, and an F1-score of 92%, indicating its robustness in identifying brain tumors. The promising results demonstrate that PBVit can play a important role in facilitating early-stage diagnosis, reducing unnecessary biopsies, and ultimately enhancing patient care, while also showcasing the potential of transformer-based architectures in medical imaging.

Classification of brain tumour is critical in medical image analysis and diagnosis as it aids in determining the affinity of cases to select treatment paths. This feature underscores the importance of accurate classification of brain tumours to predict their behavior, determine suitable management methods, and thus improve the performance of patients. The last few years have revolutionized medical picture grouping and brain tumour studies, thanks to machine learning tools, primarily CNNs. They demonstrate much potential for nearly extracting all features of interest from medical images and using them to classify tumours with high levels of accuracy. CNNs have complied with high accuracy rates, especially compared to the other forms of machine learning in this field. Due to the inherent ability to extract various aspects of the image along with the hierarchical detail of the picture, they are more suitable for use in operations like brain tumour classification. Similarly, the advancements in the field of natural language processing with transformer models have resulted in increased interest in the practical application of these types of models in image classification tasks. However, the application of ML models in the classification of brain tumours is still limited, therefore requiring continued research in this area. Therefore, the question about CNNs and Transformers’ performance differences within the framework of the brain tumour classification becomes relatively more significant. This survey aims to provide recent contributions of related work in the domain and highlight the dynamism that has emerged due to such state-of-the-art methods.