Academic Journal
SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation
| العنوان: | SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation |
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| المؤلفون: | Maria Nancy A, K. Sathyarajasekaran |
| المصدر: | Automatika, Vol 65, Iss 4, Pp 1350-1363 (2024) |
| بيانات النشر: | Taylor & Francis Group, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Automation |
| مصطلحات موضوعية: | Deep learning, Swin Transformer, brain tumour segmentation, non-local block, explainable AI, Grad-CAM, Control engineering systems. Automatic machinery (General), TJ212-225, Automation, T59.5 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 00051144 1848-3380 0005-1144 |
| Relation: | https://doaj.org/toc/0005-1144; https://doaj.org/toc/1848-3380 |
| DOI: | 10.1080/00051144.2024.2374179 |
| URL الوصول: | https://doaj.org/article/d529fe9dc5004b6ebd1d77fe0068fe89 |
| رقم الانضمام: | edsdoj.529fe9dc5004b6ebd1d77fe0068fe89 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 00051144 18483380 |
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| DOI: | 10.1080/00051144.2024.2374179 |