SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation
Brain Tumor Segmentation (BTS) and classification are important and growing research fields. Magnetic resonance imaging (MRI) is commonly used in the diagnosis of brain tumours owing to its low radiation exposure and high image quality. One of the current subjects in the field of medical imaging is...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-10-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2024.2374179 |
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| author | Maria Nancy A K. Sathyarajasekaran |
| author_facet | Maria Nancy A K. Sathyarajasekaran |
| author_sort | Maria Nancy A |
| collection | DOAJ |
| description | Brain Tumor Segmentation (BTS) and classification are important and growing research fields. Magnetic resonance imaging (MRI) is commonly used in the diagnosis of brain tumours owing to its low radiation exposure and high image quality. One of the current subjects in the field of medical imaging is how to quickly and precisely segment MRI scans of brain tumours. Unfortunately, most existing brain tumour segmentation algorithms use inadequate 2D picture segmentation methods and fail to capture the spatial correlation between features. In this study, we propose a segmentation model (SwinVNETR) Swin V-NetTRansformer-based architecture with a non-local block. This model was trained using the Brain Tumor Segmentation Challenge BraTS 2021 dataset. The Dice similarity coefficients for the enhanced tumour (ET), whole tumour (WT), and tumour core (TC) are 0.84, 0.91, and 0.87, respectively. By leveraging this methodology, we can segment brain tumours more accurately than ever before. In conclusion, we present the findings of our model through the application of the Grad-CAM methodology, an eXplainable Artificial Intelligence (XAI) technique utilized to elucidate the insights derived from the model, which helped in better understanding; doctors can better diagnose and treat patients with brain tumours. |
| format | Article |
| id | doaj-art-d529fe9dc5004b6ebd1d77fe0068fe89 |
| institution | Kabale University |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-d529fe9dc5004b6ebd1d77fe0068fe892024-11-29T06:50:32ZengTaylor & Francis GroupAutomatika0005-11441848-33802024-10-016541350136310.1080/00051144.2024.2374179SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor SegmentationMaria Nancy A0K. Sathyarajasekaran1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaBrain Tumor Segmentation (BTS) and classification are important and growing research fields. Magnetic resonance imaging (MRI) is commonly used in the diagnosis of brain tumours owing to its low radiation exposure and high image quality. One of the current subjects in the field of medical imaging is how to quickly and precisely segment MRI scans of brain tumours. Unfortunately, most existing brain tumour segmentation algorithms use inadequate 2D picture segmentation methods and fail to capture the spatial correlation between features. In this study, we propose a segmentation model (SwinVNETR) Swin V-NetTRansformer-based architecture with a non-local block. This model was trained using the Brain Tumor Segmentation Challenge BraTS 2021 dataset. The Dice similarity coefficients for the enhanced tumour (ET), whole tumour (WT), and tumour core (TC) are 0.84, 0.91, and 0.87, respectively. By leveraging this methodology, we can segment brain tumours more accurately than ever before. In conclusion, we present the findings of our model through the application of the Grad-CAM methodology, an eXplainable Artificial Intelligence (XAI) technique utilized to elucidate the insights derived from the model, which helped in better understanding; doctors can better diagnose and treat patients with brain tumours.https://www.tandfonline.com/doi/10.1080/00051144.2024.2374179Deep learningSwin Transformerbrain tumour segmentationnon-local blockexplainable AIGrad-CAM |
| spellingShingle | Maria Nancy A K. Sathyarajasekaran SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation Automatika Deep learning Swin Transformer brain tumour segmentation non-local block explainable AI Grad-CAM |
| title | SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation |
| title_full | SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation |
| title_fullStr | SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation |
| title_full_unstemmed | SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation |
| title_short | SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation |
| title_sort | swinvnetr swin v net transformer with non local block for volumetric mri brain tumor segmentation |
| topic | Deep learning Swin Transformer brain tumour segmentation non-local block explainable AI Grad-CAM |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2024.2374179 |
| work_keys_str_mv | AT marianancya swinvnetrswinvnettransformerwithnonlocalblockforvolumetricmribraintumorsegmentation AT ksathyarajasekaran swinvnetrswinvnettransformerwithnonlocalblockforvolumetricmribraintumorsegmentation |