The Impact of Replacing Complex Hand-Crafted Features with Standard Features for Melanoma Classification using Both Hand-Crafted and Deep Features
Journal article, Peer reviewed
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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.