Transformer-augmented lightweight U-Net (UAAC-Net) for accurate MRI brain tumor segmentation

Accurate segmentation of brain tumor images, particularly gliomas in MRI scans, is crucial for early diagnosis, monitoring progression, and evaluating tumor structure and therapeutic response. A novel lightweight, transformer-based U-Net model for brain tumor segmentation, integrating attention mechanisms and multi-layer feature extraction via atrous convolution to capture long-range relationships and contextual information across image regions is proposed in this work. The model performance is evaluated on the publicly accessible BraTS 2020 dataset using evaluation metrics such as the Dice coefficient, accuracy, mean Intersection over Union (IoU), sensitivity, and specificity. The proposed model outperforms many of the existing methods, such as MimicNet, Swin Transformer-based UNet and hybrid multiresolution-based UNet, and is capable of handling a variety of segmentation issues. The experimental results demonstrate that the proposed model acheives an accuracy of 98.23%, a Dice score of 0.9716, and a mean IoU of 0.8242 during training when compared to the current state-of-the-art methods.