Abstract
In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.
Highlights
Skin cancers are the most widely recognised type of growths in humans
Computer-Aided Diagnosis (CAD) tools can be connected to such devices to create a smart system that helps dermatologists recognize the melanoma
Codella et al [2] have shown in recent research that some of these CAD systems have better performance than average human expert agreements
Summary
Skin cancers are the most widely recognised type of growths in humans. They are a type of destructive disease that affects skin [1]. The early diagnosis of skin tumours can save patients. Computer-Aided Diagnosis (CAD) tools can be connected to such devices to create a smart system that helps dermatologists recognize the melanoma. Traditional melanoma detection CAD programs generally consist of three main components: the segmentation of the lesion, the extraction of functions, and the classification of features [3]. Yu et al [4] showed that melanoma patients can only be classified by means of features extracted using deep study models, the diagnostic performance is significantly improved by incorporating segmented lesions
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