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BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation
Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robus...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318134/ https://www.ncbi.nlm.nih.gov/pubmed/37408587 http://dx.doi.org/10.3389/fphys.2023.1173108 |
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author | Jiang, Jinyun Sun, Zitong Zhang, Qile Lan, Kun Jiang, Xiaoliang Wu, Jun |
author_facet | Jiang, Jinyun Sun, Zitong Zhang, Qile Lan, Kun Jiang, Xiaoliang Wu, Jun |
author_sort | Jiang, Jinyun |
collection | PubMed |
description | Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively. |
format | Online Article Text |
id | pubmed-10318134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103181342023-07-05 BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation Jiang, Jinyun Sun, Zitong Zhang, Qile Lan, Kun Jiang, Xiaoliang Wu, Jun Front Physiol Physiology Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively. Frontiers Media S.A. 2023-06-20 /pmc/articles/PMC10318134/ /pubmed/37408587 http://dx.doi.org/10.3389/fphys.2023.1173108 Text en Copyright © 2023 Jiang, Sun, Zhang, Lan, Jiang and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Jiang, Jinyun Sun, Zitong Zhang, Qile Lan, Kun Jiang, Xiaoliang Wu, Jun BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation |
title | BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation |
title_full | BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation |
title_fullStr | BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation |
title_full_unstemmed | BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation |
title_short | BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation |
title_sort | bidfdc-net: a dense connection network based on bi-directional feedback for skin image segmentation |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318134/ https://www.ncbi.nlm.nih.gov/pubmed/37408587 http://dx.doi.org/10.3389/fphys.2023.1173108 |
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