Melanoma and nevus classification based on asymmetry, border, color, and GLCM texture parameters using deep learning algorithm

Abstract

Pattern analysis has been shown to have higher reliability for melanoma and nevus classification. The ABCD method, which is common to be used as a melanoma diagnosis method, has been shown to have inappropriate weighting for each parameter. In addition, pattern analysis has been shown to have a higher success for diagnosing melanoma. In this paper, we choose the Grey Level Co-occurrence Matrix (GLCM) as a texture parameter to represent the pattern of melanoma. We also choose Deep Neural Network (DNN) to retrieve information from the data set. DNN has the capability of analyzing data with a high level of abstraction. Therefore, we choose DNN as a method to classify melanoma and nevus. We use the International Skin Imaging Collaboration (ISIC) archive database as our training and validation data. We use 773 nevi with 870 melanoma images as training data and separate 200 Nevus and 200 Melanoma images as validation data. We achieve 81.75% diagnostic accuracy, 75.5% sensitivity, and 88% specificity.