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A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

Wajiha Rahim Khan1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Majed Alhaisoni4, Usman Tariq5, Jae-Hyuk Cha6,*

1 Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
2 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Pakistan
3 Department of Computer Science, HITEC University, Taxila, Pakistan
4 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
5 Department of Management Information Systems, CoBA, Prince Sattam Bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia
6 Department of Computer Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Jae-Hyuk Cha. Email: email

(This article belongs to the Special Issue: Cancer Diagnosis using Deep Learning, Federated Learning, and Features Optimization Techniques)

Computers, Materials & Continua 2023, 76(1), 647-664. https://doi.org/10.32604/cmc.2023.039188

Abstract

Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low- and high-level features from MRI volumes. Attention and Squeeze-Excitation (SE) modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive fields. The proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867, 0.813, and 0.787, as well as a sensitivity of 0.93, 0.88, and 0.83 for Whole Tumor, Tumor Core, and Enhancing Tumor, on test dataset respectively. Experimental results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art models. Overall, the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.

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APA Style
Khan, W.R., Madni, T.M., Janjua, U.I., Javed, U., Khan, M.A. et al. (2023). A hybrid attention-based residual unet for semantic segmentation of brain tumor. Computers, Materials & Continua, 76(1), 647-664. https://doi.org/10.32604/cmc.2023.039188
Vancouver Style
Khan WR, Madni TM, Janjua UI, Javed U, Khan MA, Alhaisoni M, et al. A hybrid attention-based residual unet for semantic segmentation of brain tumor. Comput Mater Contin. 2023;76(1):647-664 https://doi.org/10.32604/cmc.2023.039188
IEEE Style
W.R. Khan et al., "A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor," Comput. Mater. Contin., vol. 76, no. 1, pp. 647-664. 2023. https://doi.org/10.32604/cmc.2023.039188



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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