The Impact of Replacing Complex Hand-Crafted Features with Standard Features for Melanoma Classification Using Both Hand-Crafted and Deep Features

Abstract

Melanoma is the deadliest form of skin cancer and it is the most rapidly spreading cancer in the world. An earlier detection of this kind of cancer is curable; hence, earlier detection of melanoma is pre-eminent. Because of this fact, a lot of research is being done in this area especially in automatic detection of melanoma. In this paper, we are proposing an automatic melanoma detection system which utilizes a combination of deep and hand-crafted features. We analyzed the impact of using a simpler and standard hand-crafted feature, in place of complex usual hand-crafted features e.g. shape, texture, diameter, or some custom features. We used a convolutional neural network (CNN) known as deep residual network (ResNet) to extract the deep features and utilized the scale invariant feature descriptor (SIFT) as the hand-crafted feature. The experiments revealed that combining SIFT did not improve the accuracy of the system however, we obtained higher accuracy than state-of-the-art methods with our deep only solution.