SepU-Net MRI Segmentation Algorithm Using Depthwise Separable Convolution and Pointwise Convolution Integrated U-Net
Corressponding author's email:
20119192@student.hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.2026.1890Keywords:
Depthwise separable convolution, Medical image segmentation, Light weight neural network, Computational efficiency, Brain tumor segmentation, Lightweight architectureAbstract
Accurate segmentation of brain tumors in MRI remains challenging due to the computational demands of conventional deep learning models. We present SepU-Net, a lightweight convolutional neural network that employs Depthwise Separable Convolutions and efficiently channel attention to reduce model complexity by 69.3% compared to standard U-Net (2.39M vs. 7.76M parameters).Evaluated on both BraTS2020 and BraTS2021, SepU-Net achieves high accuracy (0.9938 and 0.994), mean IoU (0.842 and 0.8318), and Dice coefficients (0.846 and 0.8325), with only minor declines on the more heterogeneous BraTS2021 dataset. Notably, SepU-Net delivers a 32.34% improvement in Tumor Core segmentation and a 10.14% gain in Enhancing Tumor segmentation over U-Net, while maintaining strong precision (0.994/0.9942) and sensitivity (0.9921/0.9915) across datasets. SepU-Net requires only 8.3 GFLOPs per inference, 65% fewer than U-Net and operates efficiently on embedded devices with a memory footprint of 1.4GB. These results validate its ability to balance accuracy and efficiency, enabling real-time segmentation in clinical settings. Future work will integrate attention mechanisms and extend the architecture to 3D for enhanced spatial context.
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