An Intelligent Approach for the Diagnosis of Brain Tumor by Amalgamation of Segmentation and Rendering Technique

Brain tumors, characterized by the accumulation of abnormal cells within the brain, pose a significant threat due to their potential to damage surrounding tissue. Timely and accurate identification of these tumors is crucial for effective treatment. In order to overcome the constraints associated with manual analysis, we proposed an innovative self-supervised learning framework designed for the reconstruction of 3D medical images. Our approach introduces a HyperNet structure named Meta Assistant Network (MAN), specifically crafted to navigate the extensive search space of segmentation networks. Additionally, we present a 3D transformer-based architecture, named HyperSwinNet, which combines HyperNet with an encoder extracting feature. The proposed framework's effectiveness is demonstrated through fine-tuning with the pre-trained Swin UNETR on the BRATS dataset, achieving cutting edge results on test leaderboards.