Feature extraction from dermoscopy images for melanoma diagnosis

Abstract

Melanoma is the deadliest type of skin cancer. It has been rising exponentially for the last few decades. If it is diagnosed and treated at its early stage, the survival rate is very high. To prevent the invasive biopsy technique, automated diagnosis of melanoma from dermoscopy images has become a hot research area for the last few decades. This paper proposes three new distinct and effective features with some existing features related to shape, size and color properties of dermoscopy images based on ABCD rule for melanoma detection. ABCD stands for Asymmetry, Border, Color, and Diameter of the skin lesion. A two-stage segmentation approach including Otsu algorithm and Chan–Vese algorithm for lesion segmentation is implemented in this paper. Dull-Razor algorithm removes the black and dark hair from the input images and artificial neural network classifier classifies the malignant and benign images based on the extracted features. Implementation result of the proposed approach achieves 98.2% overall classification accuracy with 98% sensitivity and 98.2% specificity. These promising results indicate that the proposed system is able to assist the dermatologists in early detection of melanoma.