TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation
Brain tumor segmentation is crucial in medical imaging, allowing informed diagnosis and treatment planning. In this study, we propose TuSegNet, a new transformer-based and attention-enhanced architecture for robust brain tumor segmentation. The model combines convolutional layers with transformer bl...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11002687/ |
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| author | Mir Nafiul Nagib Rahat Pervez Afsana Alam Nova Hadiur Rahman Nabil Zeyar Aung M. F. Mridha |
| author_facet | Mir Nafiul Nagib Rahat Pervez Afsana Alam Nova Hadiur Rahman Nabil Zeyar Aung M. F. Mridha |
| author_sort | Mir Nafiul Nagib |
| collection | DOAJ |
| description | Brain tumor segmentation is crucial in medical imaging, allowing informed diagnosis and treatment planning. In this study, we propose TuSegNet, a new transformer-based and attention-enhanced architecture for robust brain tumor segmentation. The model combines convolutional layers with transformer blocks for global context awareness, incorporates Atrous Spatial Pyramid Pooling (ASPP) for multi-scale feature extraction, and employs channel attention mechanisms to concentrate on tumor-relevant parts. Evaluated on three datasets—Dataset A, Dataset B, and a combined dataset—TuSegNet achieves state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 0.895, 0.910, and 0.930, respectively, and an Intersection over Union (IoU) of 0.820, 0.835, and 0.860. Ablation studies validate the importance of ASPP and attention mechanisms, while comparative analysis demonstrates outstanding performance over existing SOTA models such as Swin UNet and TransUNet. The proposed methodology improves segmentation accuracy and highlights the importance of hybrid architectures in handling complex medical imaging tasks. These developments underscore the potential of TuSegNet for real-world healthcare applications in brain tumor diagnosis. |
| format | Article |
| id | doaj-art-8aacc01bf30c464f9725f426a4d6a019 |
| institution | DOAJ |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-8aacc01bf30c464f9725f426a4d6a0192025-08-20T03:07:44ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01675076110.1109/OJCS.2025.356975811002687TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor SegmentationMir Nafiul Nagib0Rahat Pervez1Afsana Alam Nova2Hadiur Rahman Nabil3https://orcid.org/0009-0005-4311-2875Zeyar Aung4https://orcid.org/0000-0001-5990-9305M. F. Mridha5https://orcid.org/0000-0001-5738-1631Department of Information Technology, Washington University of Science and Technology, Alexandria, VA, USABay Atlantic University, Washington, DC, USADepartment of Information Technology, Washington University of Science and Technology, Alexandria, VA, USADepartment of Computer Science and Engineering, American International University, Dhaka, BangladeshDepartment of Computer Science, Khalifa University, Abu Dhabi, UAEDepartment of Computer Science and Engineering, American International University, Dhaka, BangladeshBrain tumor segmentation is crucial in medical imaging, allowing informed diagnosis and treatment planning. In this study, we propose TuSegNet, a new transformer-based and attention-enhanced architecture for robust brain tumor segmentation. The model combines convolutional layers with transformer blocks for global context awareness, incorporates Atrous Spatial Pyramid Pooling (ASPP) for multi-scale feature extraction, and employs channel attention mechanisms to concentrate on tumor-relevant parts. Evaluated on three datasets—Dataset A, Dataset B, and a combined dataset—TuSegNet achieves state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 0.895, 0.910, and 0.930, respectively, and an Intersection over Union (IoU) of 0.820, 0.835, and 0.860. Ablation studies validate the importance of ASPP and attention mechanisms, while comparative analysis demonstrates outstanding performance over existing SOTA models such as Swin UNet and TransUNet. The proposed methodology improves segmentation accuracy and highlights the importance of hybrid architectures in handling complex medical imaging tasks. These developments underscore the potential of TuSegNet for real-world healthcare applications in brain tumor diagnosis.https://ieeexplore.ieee.org/document/11002687/Brain tumor segmentationtransformer-based architectureattention mechanismsmedical image analysisdeep learningcomputer vision |
| spellingShingle | Mir Nafiul Nagib Rahat Pervez Afsana Alam Nova Hadiur Rahman Nabil Zeyar Aung M. F. Mridha TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation IEEE Open Journal of the Computer Society Brain tumor segmentation transformer-based architecture attention mechanisms medical image analysis deep learning computer vision |
| title | TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation |
| title_full | TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation |
| title_fullStr | TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation |
| title_full_unstemmed | TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation |
| title_short | TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation |
| title_sort | tusegnet a transformer based and attention enhanced architecture for brain tumor segmentation |
| topic | Brain tumor segmentation transformer-based architecture attention mechanisms medical image analysis deep learning computer vision |
| url | https://ieeexplore.ieee.org/document/11002687/ |
| work_keys_str_mv | AT mirnafiulnagib tusegnetatransformerbasedandattentionenhancedarchitectureforbraintumorsegmentation AT rahatpervez tusegnetatransformerbasedandattentionenhancedarchitectureforbraintumorsegmentation AT afsanaalamnova tusegnetatransformerbasedandattentionenhancedarchitectureforbraintumorsegmentation AT hadiurrahmannabil tusegnetatransformerbasedandattentionenhancedarchitectureforbraintumorsegmentation AT zeyaraung tusegnetatransformerbasedandattentionenhancedarchitectureforbraintumorsegmentation AT mfmridha tusegnetatransformerbasedandattentionenhancedarchitectureforbraintumorsegmentation |