Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning

Abstract Deep learning has become an extremely popular method in recent years, and can be a powerful tool in complex, prior-knowledge-required areas, especially in the field of biomedicine, which is now facing the problem of inadequate medical resources. The application of deep learning in disease d...

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Autores principales:	Bin Zhang, Xue Zhou, Yichen Luo, Hao Zhang, Huayong Yang, Jien Ma, Liang Ma
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Skin disease Image method Deep learning Disease classification Ocean engineering TC1501-1800 Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/7cd9e916eb9442a7bca9930766a3eb6a
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spelling	oai:doaj.org-article:7cd9e916eb9442a7bca9930766a3eb6a2021-11-28T12:03:33ZOpportunities and Challenges: Classification of Skin Disease Based on Deep Learning10.1186/s10033-021-00629-51000-93452192-8258https://doaj.org/article/7cd9e916eb9442a7bca9930766a3eb6a2021-11-01T00:00:00Zhttps://doi.org/10.1186/s10033-021-00629-5https://doaj.org/toc/1000-9345https://doaj.org/toc/2192-8258Abstract Deep learning has become an extremely popular method in recent years, and can be a powerful tool in complex, prior-knowledge-required areas, especially in the field of biomedicine, which is now facing the problem of inadequate medical resources. The application of deep learning in disease diagnosis has become a new research topic in dermatology. This paper aims to provide a quick review of the classification of skin disease using deep learning to summarize the characteristics of skin lesions and the status of image technology. We study the characteristics of skin disease and review the research on skin disease classification using deep learning. We analyze these studies using datasets, data processing, classification models, and evaluation criteria. We summarize the development of this field, illustrate the key steps and influencing factors of dermatological diagnosis, and identify the challenges and opportunities at this stage. Our research confirms that a skin disease recognition method based on deep learning can be superior to professional dermatologists in specific scenarios and has broad research prospects.Bin ZhangXue ZhouYichen LuoHao ZhangHuayong YangJien MaLiang MaSpringerOpenarticleSkin diseaseImage methodDeep learningDisease classificationOcean engineeringTC1501-1800Mechanical engineering and machineryTJ1-1570ENChinese Journal of Mechanical Engineering, Vol 34, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Skin disease Image method Deep learning Disease classification Ocean engineering TC1501-1800 Mechanical engineering and machinery TJ1-1570
spellingShingle	Skin disease Image method Deep learning Disease classification Ocean engineering TC1501-1800 Mechanical engineering and machinery TJ1-1570 Bin Zhang Xue Zhou Yichen Luo Hao Zhang Huayong Yang Jien Ma Liang Ma Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning
description	Abstract Deep learning has become an extremely popular method in recent years, and can be a powerful tool in complex, prior-knowledge-required areas, especially in the field of biomedicine, which is now facing the problem of inadequate medical resources. The application of deep learning in disease diagnosis has become a new research topic in dermatology. This paper aims to provide a quick review of the classification of skin disease using deep learning to summarize the characteristics of skin lesions and the status of image technology. We study the characteristics of skin disease and review the research on skin disease classification using deep learning. We analyze these studies using datasets, data processing, classification models, and evaluation criteria. We summarize the development of this field, illustrate the key steps and influencing factors of dermatological diagnosis, and identify the challenges and opportunities at this stage. Our research confirms that a skin disease recognition method based on deep learning can be superior to professional dermatologists in specific scenarios and has broad research prospects.
format	article
author	Bin Zhang Xue Zhou Yichen Luo Hao Zhang Huayong Yang Jien Ma Liang Ma
author_facet	Bin Zhang Xue Zhou Yichen Luo Hao Zhang Huayong Yang Jien Ma Liang Ma
author_sort	Bin Zhang
title	Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning
title_short	Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning
title_full	Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning
title_fullStr	Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning
title_full_unstemmed	Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning
title_sort	opportunities and challenges: classification of skin disease based on deep learning
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/7cd9e916eb9442a7bca9930766a3eb6a
work_keys_str_mv	AT binzhang opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning AT xuezhou opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning AT yichenluo opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning AT haozhang opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning AT huayongyang opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning AT jienma opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning AT liangma opportunitiesandchallengesclassificationofskindiseasebasedondeeplearning
_version_	1718408207076950016

Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning

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