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.
Highlights
Melanoma is a skin cancer that develops within pigment-producing skin cells called melanocytes
We propose a segmentation method to extract the skin lesion, detect its border using the Canny edge detector, derive a vector of irregularity measures to represent the irregularity of the extracted skin lesion border, and eventually use a Convolutional Neural Networks (CNNs) and Gaussian naive Bayes ensemble to automatically determine whether a lesion is considered regular or irregular based on those measures
It has been used in skin lesion segmentation in dermoscopic images (Ali, Li & Trappenberg, 2019; Ali et al, 2019)
Summary
Melanoma is a skin cancer that develops within pigment-producing skin cells called melanocytes. Prognosis is influenced by the early detection and treatment of melanoma This is reflected in better survival rates for earlier stage disease (Gershenwald et al, 2017). The approach has been verified by 1992 National Institutes of Health Consensus Conference Report on Early Melanoma, in addition to other studies published at the time (Cascinelli et al, 1987; White, Rigel & Friedman, 1991; Barnhill et al, 1992; McGovern & Litaker, 1992), and is being advertised by the American Cancer Society as a method to help the early medical evaluation of any suspicious pigmented lesions
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