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

Computers, Materials & Continua 2023年第7期76卷 647-664页

作者： Wajiha Rahim Khan Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Majed Alhaisoni Usman Tariq Jae-Hyuk Cha Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Management Information Systems CoBAPrince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Segmenting brain tumors in Magnetic Resonance Imaging(MRI)volumes is challenging due to their diffuse and irregular ***,2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled ***,3D networks can be computationally expensive and require significant training *** 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 *** 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 *** and Squeeze-Excitation(SE)modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive *** 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 *** 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 ***,the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.

关键词： MRI volumes residual Unet brats-2020 squeeze-excitation(SE)

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