دورية أكاديمية
MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation
| العنوان: | MSPAN: Multi‐scale pyramid attention network for efficient skin cancer lesion segmentation |
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| المؤلفون: | Noor Ahmed, Tan Xin, Ma Lizhuang |
| المصدر: | IET Image Processing, Vol 18, Iss 7, Pp 1667-1680 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer software |
| مصطلحات موضوعية: | biomedical imaging, cancer, computer vision, convolution, convolutional neural nets, Photography, TR1-1050, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Skin cancer is common and deadly, needs to be detected and treated properly. Deep learning algorithms like UNet have shown potential results in medical imaging. Such approaches still struggle to capture fine‐grained details and scale differences in skin lesions‐based occlusions' appearance, size etc. This research proposes a redesign UNet, the Multi‐Scale Pyramid Attention Network (MSPAN), to improve skin cancer lesion segmentation. The input data is processed at numerous scales with varied receptive fields. This enhances the network's ability to identify lesion locations by capturing local and global context. Attention approaches also help the network to suppress noise by focusing on informative features. We have evaluated MSPAN model on the publicly available ISIC2018 benchmark dataset for skin lesion segmentation. The method surpasses traditional UNet and other current methods in accuracy and effectiveness. The model also has a post‐processing to estimate lesion area for fast inference, making it suitable for extensive screening. Redesigned UNet with the Multi‐Scale Pyramid Attention Network improves skin cancer lesion segmentation. The model's ability to collect fine‐grained information and handle occlusions allows for more accurate skin cancer diagnosis and treatment. The MSPAN design can improve computer‐aided diagnosis systems and help dermatologists make precise clinical decisions. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-9667 1751-9659 |
| Relation: | https://doaj.org/toc/1751-9659; https://doaj.org/toc/1751-9667 |
| DOI: | 10.1049/ipr2.12989 |
| URL الوصول: | https://doaj.org/article/9668d8a833394e278b0acd19c25086e3 |
| رقم الأكسشن: | edsdoj.9668d8a833394e278b0acd19c25086e3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12989 |