|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images|
Jane O’Shea, Sally
|Citation:||Ali A, Li J, Yang G & Jane O’Shea S (2020) A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images. PeerJ Computer Science, 6, Art. No.: e268. https://doi.org/10.7717/peerj-cs.268|
|Abstract:||Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.|
|Rights:||© 2020 Ali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.|
|peerj-cs-268.pdf||Fulltext - Published Version||19.01 MB||Adobe PDF||View/Open|
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