Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module
Hand vein image segmentation is crucial for diverse applications such as precise biometric identification and facilitating medical intravenous procedures. This paper introduces an enhanced hand vein image segmentation method utilizing deep learning, specifically a conditional generative adversarial...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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ECTI Association
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/44346/ |
| _version_ | 1848827326893129728 |
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| author | Marlina, Yakno Mohd Zamri, Ibrahim Muhammad Salihin, Saealal Norasyikin, Fadilah Wan Nur Azhani, Wan Samsudin |
| author_facet | Marlina, Yakno Mohd Zamri, Ibrahim Muhammad Salihin, Saealal Norasyikin, Fadilah Wan Nur Azhani, Wan Samsudin |
| author_sort | Marlina, Yakno |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Hand vein image segmentation is crucial for diverse applications such as precise biometric identification and facilitating medical intravenous procedures. This paper introduces an enhanced hand vein image segmentation method utilizing deep learning, specifically a conditional generative adversarial network (cGAN). The cGAN is trained adversarially and augmented with a modied ecient channel attention (ECA) mechanism module. The efficiency of the proposed technique was evaluated using four hand vein datasets: self-acquired dataset, SUAS, WILCHES, and BOSPHORUS. Performance comparison reveals that the proposed method outperforms alternative approaches, achieving the best results across all datasets with an average sensitivity of 0.8878, average accuracy of 0.9639, and average dice coeffcient of 0.7904 for vein patterns. Our experimental findings demonstrate that the proposed segmentation technique significantly enhances hand vein patterns and improves dorsal hand vein detection accuracy. |
| first_indexed | 2025-11-15T03:58:57Z |
| format | Article |
| id | ump-44346 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:57Z |
| publishDate | 2025 |
| publisher | ECTI Association |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-443462025-08-28T00:18:15Z https://umpir.ump.edu.my/id/eprint/44346/ Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module Marlina, Yakno Mohd Zamri, Ibrahim Muhammad Salihin, Saealal Norasyikin, Fadilah Wan Nur Azhani, Wan Samsudin QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Hand vein image segmentation is crucial for diverse applications such as precise biometric identification and facilitating medical intravenous procedures. This paper introduces an enhanced hand vein image segmentation method utilizing deep learning, specifically a conditional generative adversarial network (cGAN). The cGAN is trained adversarially and augmented with a modied ecient channel attention (ECA) mechanism module. The efficiency of the proposed technique was evaluated using four hand vein datasets: self-acquired dataset, SUAS, WILCHES, and BOSPHORUS. Performance comparison reveals that the proposed method outperforms alternative approaches, achieving the best results across all datasets with an average sensitivity of 0.8878, average accuracy of 0.9639, and average dice coeffcient of 0.7904 for vein patterns. Our experimental findings demonstrate that the proposed segmentation technique significantly enhances hand vein patterns and improves dorsal hand vein detection accuracy. ECTI Association 2025-04 Article PeerReviewed pdf en cc_by_nc_nd_4 https://umpir.ump.edu.my/id/eprint/44346/1/Enhanced%20hand%20vein%20segmentation%20using%20generative%20adversarial%20network.pdf Marlina, Yakno and Mohd Zamri, Ibrahim and Muhammad Salihin, Saealal and Norasyikin, Fadilah and Wan Nur Azhani, Wan Samsudin (2025) Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module. ECTI Transactions on Computer and Information Technology, 19 (2). pp. 182-194. ISSN 2286-9131. (Published) https://doi.org/10.37936/ecti-cit.2025192.259390 https://doi.org/10.37936/ecti-cit.2025192.259390 https://doi.org/10.37936/ecti-cit.2025192.259390 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Marlina, Yakno Mohd Zamri, Ibrahim Muhammad Salihin, Saealal Norasyikin, Fadilah Wan Nur Azhani, Wan Samsudin Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module |
| title | Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module |
| title_full | Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module |
| title_fullStr | Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module |
| title_full_unstemmed | Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module |
| title_short | Enhanced hand vein segmentation using generative adversarial network integrated with modified ECA module |
| title_sort | enhanced hand vein segmentation using generative adversarial network integrated with modified eca module |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/44346/ https://umpir.ump.edu.my/id/eprint/44346/ https://umpir.ump.edu.my/id/eprint/44346/ |