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Dense Convolutional Network and Its Application in Medical Image Analysis
Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of D...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060995/ https://www.ncbi.nlm.nih.gov/pubmed/35509707 http://dx.doi.org/10.1155/2022/2384830 |
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author | Zhou, Tao Ye, XinYu Lu, HuiLing Zheng, Xiaomin Qiu, Shi Liu, YunCan |
author_facet | Zhou, Tao Ye, XinYu Lu, HuiLing Zheng, Xiaomin Qiu, Shi Liu, YunCan |
author_sort | Zhou, Tao |
collection | PubMed |
description | Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet. |
format | Online Article Text |
id | pubmed-9060995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90609952022-05-03 Dense Convolutional Network and Its Application in Medical Image Analysis Zhou, Tao Ye, XinYu Lu, HuiLing Zheng, Xiaomin Qiu, Shi Liu, YunCan Biomed Res Int Review Article Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet. Hindawi 2022-04-25 /pmc/articles/PMC9060995/ /pubmed/35509707 http://dx.doi.org/10.1155/2022/2384830 Text en Copyright © 2022 Tao Zhou et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Zhou, Tao Ye, XinYu Lu, HuiLing Zheng, Xiaomin Qiu, Shi Liu, YunCan Dense Convolutional Network and Its Application in Medical Image Analysis |
title | Dense Convolutional Network and Its Application in Medical Image Analysis |
title_full | Dense Convolutional Network and Its Application in Medical Image Analysis |
title_fullStr | Dense Convolutional Network and Its Application in Medical Image Analysis |
title_full_unstemmed | Dense Convolutional Network and Its Application in Medical Image Analysis |
title_short | Dense Convolutional Network and Its Application in Medical Image Analysis |
title_sort | dense convolutional network and its application in medical image analysis |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060995/ https://www.ncbi.nlm.nih.gov/pubmed/35509707 http://dx.doi.org/10.1155/2022/2384830 |
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