ARCUNet: enhancing skin lesion segmentation with residual convolutions and attention mechanisms for...
ARCUNet: enhancing skin lesion segmentation with residual convolutions and attention mechanisms for improved accuracy and robustness
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Skin lesion segmentation presents significant challenges due to the high variability in lesion size, shape, color, and texture and the presence of artifacts like hair, shadows, and reflections, which complicate accurate boundary delineation. To address these challenges, we proposed ARCUNet, a semantic segmentation model including residual convoluti...
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ARCUNet: enhancing skin lesion segmentation with residual convolutions and attention mechanisms for improved accuracy and robustness
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TN_cdi_doaj_primary_oai_doaj_org_article_8bc50ed67c1f4911a47351312ba7a14c
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_8bc50ed67c1f4911a47351312ba7a14c
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-94380-9
