[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119144-en":3,"doc-seo-119144-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119144,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","A review of psoriasis image analysis based on machine learning","Machine Learning (ML), including traditional machine learning and deep learning, supports medical care by learning patterns from data and improving decision-making in clinical workflows. This systematic literature review synthesizes research and applications of ML for psoriasis image analysis from the past decade. Fifty-three publications are summarized and organized into lesion localization and segmentation, lesion recognition, and lesion severity/area scoring. Common models and datasets are presented, key challenges are discussed, and future trends are explored to guide subsequent research.","TYPE Systematic Review PUBLISHED 07 August 2024  \nDOI 10. 3389/fmed.2024.1414582  \nOPEN ACCESS  \nEDITED BY  \nMonica Bianchini,  \nUniversity of Siena, Italy  \nREVIEWED BY  \nMassimo Salvi,  \nPolytechnic University of Turin, Italy Eric Munger,  \nUnited States Department of Veterans A􀀀airs, United States  \nJingwen Deng,  \nGuangzhou University of Chinese Medicine, China  \n*CORRESPONDENCE  \nHuihui Li  \n [lihh@gpnu.edu.cn](lihh@gpnu.edu.cn)[ ](lihh@gpnu.edu.cn)Chunlin Xu  \n [xuchunlin@gpnu.edu.cn](xuchunlin@gpnu.edu.cn)  \nRECEIVED 09 April 2024  \nACCEPTED 02 July 2024  \nPUBLISHED 07 August 2024  \nCITATION  \nLi H, Chen G, Zhang L, Xu C and Wen J (2024) A review of psoriasis image analysis based on machine learning. Front. Med. 11:1414582 .  \ndoi: 10.3389/fmed.2024.1414582  \nCOPYRIGHT  \n© 2024 Li, Chen, Zhang, Xu and Wen. This isan 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) and the copyright owner(s) 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.  \nA review of psoriasis image analysis based on machine learning  \nHuihui Li1*, Guangjie Chen1 , Li Zhang2,3 , Chunlin Xu1* and Ju Wen2,3  \n1 School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China, 2The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China, 3 Department of Dermatology, Guangdong Second Provincial General Hospital, Guangzhou, China  \nMachine 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 summarized 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.  \nKEYWORDS  \nmachine learning, deep learning, dermatology, psoriasis, review  \n1 Introduction  \nPsoriasis is a chronic, in􀀃ammatory and hyperproliferative skin disease with a genetic basis (1) . It can appear in any form on the arms, legs, scalp, buttocks, the folds of the skin and the trunk of the body (2) . Awareness is increasing that psoriasis as a disease is more than skin deep and that it is associated with systemic disorders, including Crohn’s disease, diabetes mellitus (notably type 2), metabolic syndrome, depression, and cancer (3) . The disease follows a lengthy course and is prone to relapse, sometimes persisting fora lifetime. Psoriasis is characterized by scaling, silver shavings, protrusion and erythema. Its severity is evaluated based on the degree of in􀀂ltration, erythema, area, epidermal desquamation/scaling and other indicators, each of which is scored according to di􀀓erent clinical manifestations (4) . Worldwide, approximately 125 million people have psoriasis, and psoriasis prevalence is highly variable across regions, ranging from 0.5% in parts of Asia to as high as 8% in Norway. In most regions, women and men are a􀀓ected equally (5) . ML has been widely developed to analyse health data, particularly medical images, to assist professionals in making decision","cbCaicyJj9Z2AEjy","https://ap.wps.com/l/cbCaicyJj9Z2AEjy","pdf",981751,1,14,"English","en",105,"# Introduction\n## Psoriasis background and clinical evaluation\n## Machine learning and deep learning in dermatology\n## Challenges and motivation for psoriasis image analysis review\n# Methods\n## Literature search and selection\n## Classification framework for psoriasis analysis tasks\n# Results\n## Lesion localization and segmentation\n## Lesion recognition\n## Lesion severity and area scoring\n# Discussion\n## Common models and datasets\n## Key challenges\n## Future trends","[{\"question\":\"What is the scope of this review on psoriasis image analysis?\",\"answer\":\"The review focuses on research and applications of machine learning for psoriasis image analysis over the past decade, based on a systematic literature search.\"},{\"question\":\"How are the included studies categorized?\",\"answer\":\"Studies are classified into three categories: lesion localization and segmentation, lesion recognition, and lesion severity and area scoring.\"},{\"question\":\"What challenges of deep learning are highlighted for psoriasis image processing?\",\"answer\":\"The text highlights data scarcity that can cause overfitting, complex models requiring long training times, and limited interpretability that makes results harder for clinicians to trust.\"}]","A review of psoriasis image analysis based on machine learning | PDF",1785722691,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-review-of-psoriasis-image-analysis-based-on-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-review-of-psoriasis-image-analysis-based-on-machine-learning/119144/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the scope of this review on psoriasis image analysis?","Question",{"text":76,"@type":77},"The review focuses on research and applications of machine learning for psoriasis image analysis over the past decade, based on a systematic literature search.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the included studies categorized?",{"text":81,"@type":77},"Studies are classified into three categories: lesion localization and segmentation, lesion recognition, and lesion severity and area scoring.",{"name":83,"@type":74,"acceptedAnswer":84},"What challenges of deep learning are highlighted for psoriasis image processing?",{"text":85,"@type":77},"The text highlights data scarcity that can cause overfitting, complex models requiring long training times, and limited interpretability that makes results harder for clinicians to trust.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]