Deep Learning in Skin Disease Image Recognition: A Review
The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the u...
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doaj-6d6087186156441794ff1757ba8f226a2021-03-30T04:31:57ZengIEEEIEEE Access2169-35362020-01-01820826420828010.1109/ACCESS.2020.30372589256314Deep Learning in Skin Disease Image Recognition: A ReviewLing-Fang Li0https://orcid.org/0000-0001-6018-5690Xu Wang1Wei-Jian Hu2https://orcid.org/0000-0002-0650-8396Neal N. Xiong3https://orcid.org/0000-0002-0394-4635Yong-Xing Du4https://orcid.org/0000-0003-1518-962XBao-Shan Li5School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, ChinaDepartment of Mathematics and Computer Science, Northeastem State University, Tahlequah, OK, USASchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, ChinaSchool of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, ChinaThe application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.https://ieeexplore.ieee.org/document/9256314/Deep learningimage recognitionreviewskin disease |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ling-Fang Li Xu Wang Wei-Jian Hu Neal N. Xiong Yong-Xing Du Bao-Shan Li |
spellingShingle |
Ling-Fang Li Xu Wang Wei-Jian Hu Neal N. Xiong Yong-Xing Du Bao-Shan Li Deep Learning in Skin Disease Image Recognition: A Review IEEE Access Deep learning image recognition review skin disease |
author_facet |
Ling-Fang Li Xu Wang Wei-Jian Hu Neal N. Xiong Yong-Xing Du Bao-Shan Li |
author_sort |
Ling-Fang Li |
title |
Deep Learning in Skin Disease Image Recognition: A Review |
title_short |
Deep Learning in Skin Disease Image Recognition: A Review |
title_full |
Deep Learning in Skin Disease Image Recognition: A Review |
title_fullStr |
Deep Learning in Skin Disease Image Recognition: A Review |
title_full_unstemmed |
Deep Learning in Skin Disease Image Recognition: A Review |
title_sort |
deep learning in skin disease image recognition: a review |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect. |
topic |
Deep learning image recognition review skin disease |
url |
https://ieeexplore.ieee.org/document/9256314/ |
work_keys_str_mv |
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