Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31211
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Automating the ABCD Rule for Melanoma Detection: A Survey
Author(s): Ali, Abder-Rahman
Li, Jingpeng
Yang, Guang
Keywords: Image processing
machine learning
melanoma detection
Issue Date: 2020
Citation: Ali 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.2020.2991034
Abstract: The 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.
DOI Link: 10.1109/access.2020.2991034
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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