Open Access

ARTICLE

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 Cha^6,*

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:

(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

Received 13 January 2023; Accepted 10 March 2023; Issue published 08 June 2023

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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