Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31320
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images
Author(s): Ali, Abder-Rahman
Li, Jingpeng
O’Shea, Sally Jane
Keywords: General Biochemistry, Genetics and Molecular Biology
General Agricultural and Biological Sciences
General Medicine
Issue Date: 16-Jun-2020
Citation: Ali A, Li J & O’Shea SJ (2020) Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images. PLOS ONE, 15 (6), Art. No.: e0234352. https://doi.org/10.1371/journal.pone.0234352
Abstract: Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret’s diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.
DOI Link: 10.1371/journal.pone.0234352
Rights: © 2020 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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