SepU-Net MRI Segmentation Algorithm Using Depthwise Separable Convolution and Pointwise Convolution Integrated U-Net

Các tác giả

Email tác giả liên hệ:

20119192@student.hcmute.edu.vn

DOI:

https://doi.org/10.54644/jte.2026.1890

Từ khóa:

Depthwise separable convolution, Medical image segmentation, Light weight neural network, Computational efficiency, Brain tumor segmentation, Lightweight architecture

Tóm tắt

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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Tiểu sử của Tác giả

Phong Mai Hong, Ho Chi Minh City University of Technology and Engineering, Vietnam

Phong Mai Hong is currently an undergraduate student in Computer Engineering at Ho Chi Minh City University of Technology and Engineering (formerly Ho Chi Minh City University of Technology and Education), Ho Chi Minh City, Vietnam. His academic interests include medical image processing, deep learning, and lightweight neural network architectures for embedded and clinical applications.

Phone number: 0865243215. Email: 20119192@student.hcmute.edu.vn. ORCID:  https://orcid.org/0009-0003-9741-5092

Lam Mai Thanh, Ho Chi Minh City University of Technology and Engineering, Vietnam

Lam Mai Thanh received his bachelor’s degree in computer engineering from Ho Chi Minh City University of Technology and Education (HCMUTE) (currently Ho Chi Minh City University of Technology and Engineering), Ho Chi Minh City, Vietnam. His academic interests include medical image processing, deep learning, and intelligent systems.

Phone number: 0865243215. Email: 20119137@student.hcmute.edu.vn. ORCID:  https://orcid.org/0009-0003-0896-6213

Lam Nguyen Ngo, Ho Chi Minh City University of Technology and Engineering, Vietnam

Lam Nguyen Ngo is currently a lecturer at the Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Engineering (formerly Ho Chi Minh City University of Technology and Education). He received his bachelor’s and master’s degree in radio and electronics engineering from the Ho Chi Minh City University of Technology, Vietnam in 2000 and 2004 respectively. His research interests include wireless communication, data communication, digital signal processing, and computers.

Email: lamnn@hcmute.edu.vn. ORCID:  https://orcid.org/0009-0002-6580-0175

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Tải xuống

Đã Xuất bản

2026-02-28

Cách trích dẫn

[1]
Phong Mai Hong, Lam Mai Thanh, và Lam Nguyen Ngo, “SepU-Net MRI Segmentation Algorithm Using Depthwise Separable Convolution and Pointwise Convolution Integrated U-Net”, JTE, vol 21, số p.h 01, tr 35–46, tháng 2 2026.

Số

T. 21 S. 01 (2026)

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