Novel computational approaches for border irregularity prediction to detect melanoma in skin lesions

Abstract

Medical image detection has been a rapidly growing field of study during the last few years. There are different challenges associated with it. Many works have been done in order to provide solutions for key challenges. This study of work is focused on melanoma detection by using Asymmetry, Border irregularity, Colour textures, and Diameter (ABCD) feature along with proposing two new approaches for border irregularity detection. The proposed two new approaches are distance difference method and gradient method, which follows the main concept as traversing along the continuous borderline of the lesion. Further, this study varies from the existing studies, since it has been taken counts of distances from the centroid to the borderline without considering the distance from the image border to the borderline of the lesion. It was able to achieve a classification rate of 79% and 78.5% using distance difference method and gradient method, respectively whereas the classification without the border irregularity feature achieved 78% of accuracy performing on PH2 dataset. Further, this study can be stated as most appropriate to classify non-melanoma rather than melanoma. It is contributed by generating simple computer science-based approaches rather than complex mathematical methods to detect border irregularity and makes the medical image detection easy.