A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images.

Journal: PeerJ. Computer science
Published Date: Jun 29, 2020

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 -score of 93.6%, 100%, 92.5% and 96.1%, respectively.

Authors

  • Abder-Rahman Ali
    Faculty of Natural Sciences, Computing Science and Mathematics, University of Stirling, Stirling, UK.
  • Jingpeng Li
    Faculty of Natural Sciences, Computing Science and Mathematics, University of Stirling, Stirling, UK.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Sally Jane O'Shea
    Mater Private Hospital, Cork, Ireland.

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