Combining ABCD Rule, Texture Features and Transfer Learning in Automatic Diagnosis of Melanoma

Abstract

Melanoma is a malignant skin lesion, and it is currently among the most dangerous existing cancers. However, early diagnosis of this disease gives the patient a higher chance of cure. In this work, a computational method was designed to assist dermatologists in the diagnosis of skin lesions as melanoma or non-melanoma using dermoscopic images. We conducted an extensive study to define the best set of attributes for image representation. In total, we evaluated 12,705 characteristics and three classifiers. The proposed approach aims to classify skin lesions using a hybrid descriptor obtained by combining features of color, shape, texture and pre-trained Convolutional Neural Networks. These characteristics are used as inputs to a MultiLayer Perceptron classifier. The results are promising, reaching an accuracy of 92.1% and a Kappa index of 0.8346 in 406 images from two public image databases.