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

被引:0
|
作者
Bin Zhang
Xue Zhou
Yichen Luo
Hao Zhang
Huayong Yang
Jien Ma
Liang Ma
机构
[1] Zhejiang University,State Key Laboratory of Fluid Power and Mechatronic Systems
[2] Zhejiang University,School of Mechanical Engineering
[3] Zhejiang University,College of Electrical Engineering
关键词
Skin disease; Image method; Deep learning; Disease classification;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
相关论文
共 50 条
  • [41] Deep Learning-Based Skin Diseases Classification using Smartphones
    Oztel, Ismail
    Oztel, Gozde Yolcu
    Sahin, Veysel Harun
    ADVANCED INTELLIGENT SYSTEMS, 2023, 5 (12)
  • [42] A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
    Macas, Mayra
    Wu, Chunming
    Fuertes, Walter
    COMPUTER NETWORKS, 2022, 212
  • [43] State-of-the-art skin disease classification: a review of deep learning models
    Oluwayemisi Jaiyeoba
    Emeka Ogbuju
    Grace Ataguba
    Oluwaseyi Jaiyeoba
    James Daniel Omaye
    Innocent Eze
    Francisca Oladipo
    Network Modeling Analysis in Health Informatics and Bioinformatics, 14 (1)
  • [44] Deep learning for intelligent IoT: Opportunities, challenges and solutions
    Bin Zikria, Yousaf
    Afzal, Muhammad Khalil
    Kim, Sung Won
    Marin, Andrea
    Guizani, Mohsen
    COMPUTER COMMUNICATIONS, 2020, 164 : 50 - 53
  • [45] Deep Reinforcement Learning for Quantitative Trading: Challenges and Opportunities
    An, Bo
    Sun, Shuo
    Wang, Rundong
    IEEE INTELLIGENT SYSTEMS, 2022, 37 (02) : 23 - 26
  • [46] Deep Learning for Wireless Physical Layer: Opportunities and Challenges
    Tianqi Wang
    Chao-Kai Wen
    Hanqing Wang
    Feifei Gao
    Tao Jiang
    Shi Jin
    China Communications, 2017, 14 (11) : 92 - 111
  • [47] Deep Learning for Community Detection: Progress, Challenges and Opportunities
    Liu, Fanzhen
    Xue, Shan
    Wu, Jia
    Zhou, Chuan
    Hu, Wenbin
    Paris, Cecile
    Nepal, Surya
    Yang, Jian
    Yu, Philip S.
    PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, : 4981 - 4987
  • [48] Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities
    Pang, Guansong
    Cao, Longbing
    Aggarwal, Charu
    WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2021, : 1127 - 1130
  • [49] Deep learning technologies for shield tunneling: Challenges and opportunities
    Zhou, Cheng
    Gao, Yuyue
    Chen, Elton J.
    Ding, Lieyun
    Qin, Wenbo
    AUTOMATION IN CONSTRUCTION, 2023, 154
  • [50] Deep Learning for Wireless Physical Layer: Opportunities and Challenges
    Wang, Tianqi
    Wen, Chao-Kai
    Wang, Hanqing
    Gao, Feifei
    Jiang, Tao
    Jin, Shi
    CHINA COMMUNICATIONS, 2017, 14 (11) : 92 - 111