Jia Qin Ngu0000-0003-2026-5666Nisar, HumairaHumairaNisarChi-Yi Tsai2025-10-162025-10-162025-03-2810.3390/math13071117https://dspace-cris.utar.edu.my/handle/123456789/11526This study focuses on the major challenges in ensuring the timely assessment and accurate diagnosis of brain tumors (BTs), which are essential for effective patient treatment. Hence, in this paper, a time-efficient, automated, and advanced deep learning (DL) solution, the Mamba Swin Transformer BT Segmentation (MSTransBTS) model, is introduced. This model employs the advanced Swin Transformer architecture, which is renowned for capturing long-range information and incorporates the latest Mamba approach for efficient long-range dependency modelling. Through meticulous customization and fine-tuning, the MSTransBTS achieves notable improvements in Dice scores, with scores of 89.53% for whole tumours (WTs), 80.09% for enhancing tumours (ETs), and 84.75% for tumour cores (TCs), resulting in an overall average Dice score of 84.79%. The employment of Test-Time Augmentation (TTA) further enhances performance and marks a significant advancement in BT segmentation accuracy. These findings not only address the critical need for timely assessment and diagnosis, but also emphasize the potential to enhance patient care through the automation of BT detection. By combining the features of Swin Transformer and Mamba techniques, this approach delivers a promising solution for accurate and efficient BT segmentation, which contributes to advancements in medical imaging.enbrain tumour (BT) detectionclassificationdeep learning (DL)MR imagesMambasegmentationswin transformertest-time augmentation (TTA)journal-article