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arXiv Computer Vision By Sheekar Banerjee, Md. Srabon Chowdhury, Md. Mahbub Hasan Akash, Ishtiak Al Mamoon

Lightweight Vision Transformer-Based U-Net for Brain Tumor Segmentation from MRI

Read the original on arXiv Computer Vision →

The paper introduces a lightweight Vision Transformer‑based U‑Net for brain tumor segmentation from MRI, combining U‑Net’s hierarchical feature extraction with a compact ViT bottleneck to capture both local and global context. With only 2.6 million trainable parameters, the model achieves a mean Intersection over Union of 0.8100 and a Dice score of 0.8446 on the TCGA LGG dataset, surpassing the baseline U‑Net by 3.75% and 3.15% respectively. Extensive quantitative and qualitative analyses, including confusion matrices, precision‑recall curves, and tumor size dependency studies, demonstrate the method’s effectiveness and robustness.

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