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REVIEW article

Front. Med.
Sec. Dermatology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1414582
This article is part of the Research Topic Deep Learning for Medical Imaging Applications View all articles

A Review of Psoriasis Image Analysis Based on Machine Learning

Provisionally accepted
Li Zhang Li Zhang 1*Guangjie Chen Guangjie Chen 2Huihui Li Huihui Li 2*Chunlin Xu Chunlin Xu 2*Ju Wen Ju Wen 3
  • 1 Department of Dermatology, Ningbo No 6 Hospital, Ningbo, 315040, China, Ningbo, Zhejiang Province, China
  • 2 School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China, Guangzhou, China
  • 3 Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, China

The final, formatted version of the article will be published soon.

    Machine Learning (ML), an Artificial Intelligence (AI) technique that includes both Traditional Machine Learning (TML) and Deep Learning (DL), aims to teach machines to automatically learn tasks by inferring patterns from data. It holds significant promise in aiding medical care and has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. This paper presents the findings of a systematic literature review focusing on the research and application of ML in psoriasis analysis over the past decade. We summarised 53 publications by searching the Web of Science, PubMed and IEEE Xplore databases and classified them into three categories: (i) lesion localization and segmentation; (ii) lesion recognition; (iii) lesion severity and area scoring. We have presented the most common models and datasets for psoriasis analysis, discussed the key challenges, and explored future trends in ML within this field. Our aim is to suggest directions for subsequent research.

    Keywords: machine learning, deep learning, Dermatology, Psoriasis, review

    Received: 09 Apr 2024; Accepted: 02 Jul 2024.

    Copyright: © 2024 Zhang, Chen, Li, Xu and Wen. 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) or licensor 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.

    * Correspondence:
    Li Zhang, Department of Dermatology, Ningbo No 6 Hospital, Ningbo, 315040, China, Ningbo, Zhejiang Province, China
    Huihui Li, School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China, Guangzhou, China
    Chunlin Xu, School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China, Guangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.