Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/10463
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Arsalan, Muhammad | en_US |
dc.contributor.author | Khan, Tariq M. | en_US |
dc.contributor.author | Naqvi, Syed Saud | en_US |
dc.contributor.author | Dr. NAWAZ Mehmood | en_US |
dc.contributor.author | Razzak, Imran | en_US |
dc.date.accessioned | 2024-09-07T06:28:03Z | - |
dc.date.available | 2024-09-07T06:28:03Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, vol. 20(2), pp. 1363-1371. | en_US |
dc.identifier.issn | 1545-5963 | - |
dc.identifier.issn | 1557-9964 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11861/10463 | - |
dc.description.abstract | Achieving accurate retinal vessel segmentation is critical in the progression and diagnosis of vision-threatening diseases such as diabetic retinopathy and age-related macular degeneration. Existing vessel segmentation methods are based on encoder-decoder architectures, which frequently fail to take into account the retinal vessel structure's context in their analysis. As a result, such methods have difficulty bridging the semantic gap between encoder and decoder characteristics. This paper proposes a Prompt Deep Light-weight Vessel Segmentation Network (PLVS-Net) to address these issues by using prompt blocks. Each prompt block use combination of asymmetric kernel convolutions, depth-wise separable convolutions, and ordinary convolutions to extract useful features. This novel strategy improves the performance of the segmentation network while simultaneously decreasing the number of trainable parameters. Our method outperformed competing approaches in the literature on three benchmark datasets, including DRIVE, STARE, and CHASE. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE/ACM Transactions on Computational Biology and Bioinformatics | en_US |
dc.title | Prompt deep light-weight vessel segmentation network (PLVS-Net) | en_US |
dc.type | Peer Reviewed Journal Article | en_US |
dc.identifier.doi | 10.1109/TCBB.2022.3211936 | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Department of Applied Data Science | - |
Appears in Collections: | Applied Data Science - Publication |
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