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.

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

“…Majumder and Ullah () presented the novel and effective features related to size, color, and shape of the lesion based on the ABCD rule. They implement a two‐stage segmentation method including Chan‐Vese and Otsu algorithm.…”

“…The prime objective of their work is to evaluate the efficiency of the presented lesion segmentation method and extract the most distinctive feature that discriminates the melanoma from benign. Majumder and Ullah (2019) presented the novel and effective features related to size, color, and shape of the lesion based on the ABCD rule. They implement a two-stage segmentation method including Chan-Vese and Otsu algorithm.…”

“…The more recent, various computerized methods are presented for detection and recognition of melanoma using dermoscopy data (Majumder & Ullah, ; Sreelatha, Subramanyam, & Prasad, ). Through dermoscopy, the skin images are captured, which gives more clarity of lesion spots.…”

“…The more recent, various computerized methods are presented for detection and recognition of melanoma using dermoscopy data (Majumder & Ullah, 2019;Sreelatha, Subramanyam, & Prasad, 2019).…”

“…But still, several challenges exist which decreased the accuracies of these automated systems. In general, a simple automated system follows these steps: preprocessing of original data, detection of the lesion through segmentation or featuresbased techniques, and finally classification by extracting patterns information (Jadooki, Mohamad, Saba, Almazyad, & Rehman, 2016;Majumder & Ullah, 2019;Marka, Carter, Toto, & Hassanpour, 2019;Sharif et al, 2017). A lot of techniques are presented for contrast enhancement in this domain to improve the visual quality of lesion in the given images.…”