[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124661-en":3,"doc-seo-124661-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},124661,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Application of Machine Learning in Melanoma Detection and the Identification of Ugly Duckling and Suspicious Naevi - A Review","Skin lesions called naevi vary widely in size, shape, and colouration, making melanoma identification challenging. The concept of the “Ugly Duckling Naevus” helps flag a lesion whose appearance differs from other lesions on the same individual, potentially signalling melanoma risk. This review explains how AI-driven computer-aided diagnosis combines machine learning with clinical analysis to improve accuracy and support decision-making amid limited specialist availability, emphasizing the importance of early detection for better outcomes.","Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: AReview  \nFatima Al Zegair1, Nathasha Naranpanawa1, Brigid Betz-Stablein2, Monika Janda3, H. Peter  \nSoyer2and Shekhar S. Chandra1  \n1 School of Electrical Engineering and Computer Science, University of Queensland, Brisbane, QLD, Australia  \n2 Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia  \n3Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane,  \nQLD, Australia  \nAbstract  \nSkin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an \"Ugly Duckling Naevus\" comes into play when monitoring for melanoma, referring to a lesion with distinctive features that sets it apart from other lesions in the vicinity. As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma. Artificial intelligence (AI) involves the use of computer systems to imitate intelligent behaviour, aiming to minimize human intervention. Computeraided diagnosis (CAD) has become a significant player in the research and development field, as it combines machine learning techniques with a variety of patient analysis methods. Its aim is to increase accuracy and simplify decision-making, all while responding to the shortage of specialized professionals. These automated systems are especially important in skin cancer diagnosis where specialist availability is limited. As a result, their use could lead to life-saving benefits and cost reductions within healthcare. Given the drastic change in survival when comparing early stage to late-stage melanoma, early detection is vital for effective treatment and patient outcomes. Machine learning (ML) and deep learning (DL) techniques have gained popularity in skin cancer classification, effectively addressing challenges, and providing results equivalent to that of specialists. Despite these advancements, reviews on ML and DL approaches for identifying suspicious naevi and ugly duckling (UD) naevus are limited. This article provides an extensive overview of cutting-edge ML and DL-based algorithms for melanoma detection and the identification of suspicious naevi and UD naevus. The article commences with general information on skin cancer, melanoma, and various naevus types. It then presents an overview of AI, ML, DL, and CAD, followed by successful applications of different ML techniques including convolutional neural networks (CNN) for melanoma detection, comparing them with dermatologists' performance. Lastly, the article discusses ML methods for UD naevus detection and the identification of suspicious naevi.  \n1. Introduction  \nSkin cancer is recognized as a prevailing and significant form of human malignancy, exhibiting a considerable global burden in terms of incidence rates [1] . Skin cancer cells are categorized into different types, including Basel cell carcinoma (BCC), Squamous cell  \ncarcinoma (SCC), and melanoma [2] .Visual examination stands as the primary modality employed in the diagnostic process, involving an initial clinical screening to detect potential skin cancer lesions. Subsequently, a dermoscopic analysis may be employed to further evaluate these lesions, while biopsy and subsequent histopathological examination serve as confirmatory measures if required for definitive diagnosis.  \nMelanoma, as an individual category within the spectrum of skin cancers, assumes exceptional significance, making a substantial contribution to the mortality rates associated with this disease. Its status as the most deadly subtype of skin cancer stems from its aggressive characteristics, characterized by rapid dissemination to distant anatomical sites when timely detection and treatment are lacking [3]. Exposure to both natur","cbCaihvcg6QGpBBC","https://ap.wps.com/l/cbCaihvcg6QGpBBC","pdf",641660,1,29,"English","en",105,"# Abstract\n# Introduction\n## Skin cancer and melanoma background\n## Diagnostic workflow and clinical challenge\n## Naevi characteristics and melanoma risk factors\n# Machine learning and deep learning overview (CAD context)\n## CNN-based melanoma detection and comparison with dermatologists","[{\"question\":\"What is the “Ugly Duckling” naevus concept in melanoma monitoring?\",\"answer\":\"It refers to a lesion with distinctive features that differs from other lesions on the same person. Because lesions in one individual tend to follow predictable patterns, the unusual lesion may indicate melanoma risk.\"},{\"question\":\"Why are AI and computer-aided diagnosis important for skin cancer detection?\",\"answer\":\"AI-driven CAD systems aim to reduce reliance on scarce specialist expertise while improving diagnostic accuracy. They integrate machine learning with patient analysis to support more efficient decision-making.\"},{\"question\":\"How does early detection affect melanoma outcomes?\",\"answer\":\"Early detection is crucial because melanoma survival drops drastically when comparing early-stage to late-stage disease. Prompt identification and treatment can enable cure in a high proportion of cases.\"}]","Application of Machine Learning in Melanoma Detection and the Identification of Ugly Duckling and Suspicious Naevi - A Review | PDF",1785893627,73,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-in-melanoma-detection-and-the-identification-of-ugly-duckling-and-suspicious-naevi-a-review","",{"@graph":36,"@context":85},[37,54,68],{"@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/application-of-machine-learning-in-melanoma-detection-and-the-identification-of-ugly-duckling-and-suspicious-naevi-a-review/124661/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the “Ugly Duckling” naevus concept in melanoma monitoring?","Question",{"text":75,"@type":76},"It refers to a lesion with distinctive features that differs from other lesions on the same person. Because lesions in one individual tend to follow predictable patterns, the unusual lesion may indicate melanoma risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are AI and computer-aided diagnosis important for skin cancer detection?",{"text":80,"@type":76},"AI-driven CAD systems aim to reduce reliance on scarce specialist expertise while improving diagnostic accuracy. They integrate machine learning with patient analysis to support more efficient decision-making.",{"name":82,"@type":73,"acceptedAnswer":83},"How does early detection affect melanoma outcomes?",{"text":84,"@type":76},"Early detection is crucial because melanoma survival drops drastically when comparing early-stage to late-stage disease. 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