LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution
<p>The rising occurrence and notable public health consequences of skin cancer, especially of the most challenging form known as melanoma, have created an urgent demand for more advanced approaches to disease management. The integration of modern computer vision methods into clinical procedure...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513519800549376 |
|---|---|
| author | Sadia Din (18136919) |
| author2 | Omar Mourad (6553316) Erchin Serpedin (3706543) |
| author2_role | author author |
| author_facet | Sadia Din (18136919) Omar Mourad (6553316) Erchin Serpedin (3706543) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sadia Din (18136919) Omar Mourad (6553316) Erchin Serpedin (3706543) |
| dc.date.none.fl_str_mv | 2024-03-18T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2024.108303 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/LSCS-Net_A_lightweight_skin_cancer_segmentation_network_with_densely_connected_multi-rate_atrous_convolution/25480048 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Skin lesion segmentation Multi-rate atrous convolution Residual connections |
| dc.title.none.fl_str_mv | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The rising occurrence and notable public health consequences of skin cancer, especially of the most challenging form known as melanoma, have created an urgent demand for more advanced approaches to disease management. The integration of modern computer vision methods into clinical procedures offers the potential for enhancing the detection of skin cancer . The UNet model has gained prominence as a valuable tool for this objective, continuously evolving to tackle the difficulties associated with the inherent diversity of dermatological images. These challenges stem from diverse medical origins and are further complicated by variations in lighting, patient characteristics, and hair density. In this work, we present an innovative end-to-end trainable network crafted for the segmentation of skin cancer . This network comprises an encoder–decoder architecture, a novel feature extraction block, and a densely connected multi-rate Atrous convolution block. We evaluated the performance of the proposed lightweight skin cancer segmentation network (LSCS-Net) on three widely used benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, and ISIC 2018. The generalization capabilities of LSCS-Net are testified by the excellent performance on breast cancer and thyroid nodule segmentation datasets. The empirical findings confirm that LSCS-net attains state-of-the-art results, as demonstrated by a significantly elevated Jaccard index.</p><h2>Other Information</h2> <p> Published in: Computers in Biology and Medicine<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2024.108303" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2024.108303</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_82505491cb331c528de457927e4289ba |
| identifier_str_mv | 10.1016/j.compbiomed.2024.108303 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25480048 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolutionSadia Din (18136919)Omar Mourad (6553316)Erchin Serpedin (3706543)Health sciencesHealth services and systemsSkin lesion segmentationMulti-rate atrous convolutionResidual connections<p>The rising occurrence and notable public health consequences of skin cancer, especially of the most challenging form known as melanoma, have created an urgent demand for more advanced approaches to disease management. The integration of modern computer vision methods into clinical procedures offers the potential for enhancing the detection of skin cancer . The UNet model has gained prominence as a valuable tool for this objective, continuously evolving to tackle the difficulties associated with the inherent diversity of dermatological images. These challenges stem from diverse medical origins and are further complicated by variations in lighting, patient characteristics, and hair density. In this work, we present an innovative end-to-end trainable network crafted for the segmentation of skin cancer . This network comprises an encoder–decoder architecture, a novel feature extraction block, and a densely connected multi-rate Atrous convolution block. We evaluated the performance of the proposed lightweight skin cancer segmentation network (LSCS-Net) on three widely used benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, and ISIC 2018. The generalization capabilities of LSCS-Net are testified by the excellent performance on breast cancer and thyroid nodule segmentation datasets. The empirical findings confirm that LSCS-net attains state-of-the-art results, as demonstrated by a significantly elevated Jaccard index.</p><h2>Other Information</h2> <p> Published in: Computers in Biology and Medicine<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2024.108303" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2024.108303</a></p>2024-03-18T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2024.108303https://figshare.com/articles/journal_contribution/LSCS-Net_A_lightweight_skin_cancer_segmentation_network_with_densely_connected_multi-rate_atrous_convolution/25480048CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/254800482024-03-18T03:00:00Z |
| spellingShingle | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution Sadia Din (18136919) Health sciences Health services and systems Skin lesion segmentation Multi-rate atrous convolution Residual connections |
| status_str | publishedVersion |
| title | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| title_full | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| title_fullStr | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| title_full_unstemmed | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| title_short | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| title_sort | LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution |
| topic | Health sciences Health services and systems Skin lesion segmentation Multi-rate atrous convolution Residual connections |