Extraction of Shape and Texture Features of Dermoscopy Image for Skin Cancer Identification

Abstract

Skin diseases are increasing and becoming a very serious problem. Skin cancer in general there are 2, namely melanoma and non-melanoma. Cases that are often encountered are in non-melanoma types. A critical factor in the treatment of skin cancer is early diagnosis. Doctors usually use the biopsy method to detect skin cancer. Computer-based technology provides convenient, cheaper, and faster diagnosis of skin cancer symptoms. This study aims to identify the type of skin cancer. The data used in the study were 6 types of skin cancer, namely Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus image, Pigmented Benign Keratosis image, or Vascular Lesion, with a total of 60 dermoscopy images obtained from the Kaggle site. Dermoscopy image processing begins with a pre-processing process, which converts RGB images to LAB. After that, segmentation is carried out to separate objects from the background. The method of extracting shape and texture features is used to obtain the characteristics of dermoscopy images. As many as 2 types of shape features, namely eccentricity and metric, and 4 types of texture features, namely contrast, correlation, energy, and homogeneity. The result of this study is that it can identify the type of skin cancer based on image features that have been extracted using a program from the Matlab application. The technique of extracting shape and texture features is proven to work well in identifying the type of skin cancer. In the future it is expected to use more data, and add color features in identifying dermoscopy images.