Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation
Segmentation of hand vein images is crucial for various applications, including precise biometric identification and facilitating medical intravenous procedures. This study introduces a method for hand vein image segmentation using deep learning, specifically a conditional generative adversarial net...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29113/1/Optimal%20Integration%20of%20Improved%20ECA%20Module%20in%20cGAN%20Architecture%20for%20Hand%20Vein%20Segmentation.pdf http://eprints.utem.edu.my/id/eprint/29113/ https://ieeexplore.ieee.org/document/10845353 |
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| author | Yakno, Marlina Mohd Zamri, Ibrahim Saealal, Muhammad Salihin Fadilah, Norasyikin Samsudin, Wan NUr Azhani W. |
| author_facet | Yakno, Marlina Mohd Zamri, Ibrahim Saealal, Muhammad Salihin Fadilah, Norasyikin Samsudin, Wan NUr Azhani W. |
| author_sort | Yakno, Marlina |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Segmentation of hand vein images is crucial for various applications, including precise biometric identification and facilitating medical intravenous procedures. This study introduces a method for hand vein image segmentation using deep learning, specifically a conditional generative adversarial network (cGAN). The cGAN is trained adversarially and enhanced with a modified Efficient Channel Attention (ECA) mechanism. The effectiveness of this approach is evaluated using two hand vein datasets: one sourced internally and the other from SUAS. Comparative analysis demonstrates that our method achieves superior sensitivity, accuracy, and dice coefficient on the self-acquired dataset, as well as improved sensitivity and accuracy on the SUAS dataset. Experimental results highlight the significant capability of our segmentation technique in enhancing hand vein patterns and improving the accuracy of dorsal hand vein detection. |
| format | Conference or Workshop Item |
| id | my.utem.eprints-29113 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my.utem.eprints-291132025-11-06T04:05:30Z http://eprints.utem.edu.my/id/eprint/29113/ Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation Yakno, Marlina Mohd Zamri, Ibrahim Saealal, Muhammad Salihin Fadilah, Norasyikin Samsudin, Wan NUr Azhani W. Segmentation of hand vein images is crucial for various applications, including precise biometric identification and facilitating medical intravenous procedures. This study introduces a method for hand vein image segmentation using deep learning, specifically a conditional generative adversarial network (cGAN). The cGAN is trained adversarially and enhanced with a modified Efficient Channel Attention (ECA) mechanism. The effectiveness of this approach is evaluated using two hand vein datasets: one sourced internally and the other from SUAS. Comparative analysis demonstrates that our method achieves superior sensitivity, accuracy, and dice coefficient on the self-acquired dataset, as well as improved sensitivity and accuracy on the SUAS dataset. Experimental results highlight the significant capability of our segmentation technique in enhancing hand vein patterns and improving the accuracy of dorsal hand vein detection. 2024 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29113/1/Optimal%20Integration%20of%20Improved%20ECA%20Module%20in%20cGAN%20Architecture%20for%20Hand%20Vein%20Segmentation.pdf Yakno, Marlina and Mohd Zamri, Ibrahim and Saealal, Muhammad Salihin and Fadilah, Norasyikin and Samsudin, Wan NUr Azhani W. (2024) Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation. In: 2004 IEEE 10th Information Technology International Seminar (ITIS), 06-08 November 2024, Surabaya, Indonesia. https://ieeexplore.ieee.org/document/10845353 |
| spellingShingle | Yakno, Marlina Mohd Zamri, Ibrahim Saealal, Muhammad Salihin Fadilah, Norasyikin Samsudin, Wan NUr Azhani W. Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation |
| title | Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation |
| title_full | Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation |
| title_fullStr | Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation |
| title_full_unstemmed | Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation |
| title_short | Optimal integration of improved ECA module in cGAN architecture for hand vein segmentation |
| title_sort | optimal integration of improved eca module in cgan architecture for hand vein segmentation |
| url | http://eprints.utem.edu.my/id/eprint/29113/1/Optimal%20Integration%20of%20Improved%20ECA%20Module%20in%20cGAN%20Architecture%20for%20Hand%20Vein%20Segmentation.pdf http://eprints.utem.edu.my/id/eprint/29113/ https://ieeexplore.ieee.org/document/10845353 |
| url_provider | http://eprints.utem.edu.my/ |