Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31211
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dc.contributor.authorAli, Abder-Rahmanen_UK
dc.contributor.authorLi, Jingpengen_UK
dc.contributor.authorYang, Guangen_UK
dc.date.accessioned2020-05-30T00:01:29Z-
dc.date.available2020-05-30T00:01:29Z-
dc.date.issued2020en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31211-
dc.description.abstractThe ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule.en_UK
dc.language.isoenen_UK
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_UK
dc.relationAli A, Li J & Yang G (2020) Automating the ABCD Rule for Melanoma Detection: A Survey. IEEE Access, 8, pp. 83333-83346. https://doi.org/10.1109/access.2020.2991034en_UK
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectImage processingen_UK
dc.subjectmachine learningen_UK
dc.subjectmelanoma detectionen_UK
dc.titleAutomating the ABCD Rule for Melanoma Detection: A Surveyen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1109/access.2020.2991034en_UK
dc.citation.jtitleIEEE Accessen_UK
dc.citation.issn2169-3536en_UK
dc.citation.volume8en_UK
dc.citation.spage83333en_UK
dc.citation.epage83346en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.date29/04/2020en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationImperial College Londonen_UK
dc.identifier.isiWOS:000549502200135en_UK
dc.identifier.scopusid2-s2.0-85084926933en_UK
dc.identifier.wtid1610632en_UK
dc.contributor.orcid0000-0002-5450-5472en_UK
dc.contributor.orcid0000-0002-6758-0084en_UK
dc.date.accepted2020-04-25en_UK
dcterms.dateAccepted2020-04-25en_UK
dc.date.filedepositdate2020-05-29en_UK
rioxxterms.apcpaiden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorAli, Abder-Rahman|0000-0002-5450-5472en_UK
local.rioxx.authorLi, Jingpeng|0000-0002-6758-0084en_UK
local.rioxx.authorYang, Guang|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2020-05-29en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2020-05-29|en_UK
local.rioxx.filename09079806.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2169-3536en_UK
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