Automatic Skin Cancer (Melanoma) Detection by Processing Dermatoscopic images

Melanoma is the deadliest form of skin cancer if not treated early. The best way to cure melanoma is to treat it in its earliest stage of development. Since melanoma is similar to benign moles in its shape and appearance, it is often mistaken for moles and left untreated. Automatic melanoma detection is an essential way to increase the survival rate of patients by detecting melanoma in its early stages. In this paper, a new method for automatic diagnosis of melanoma using segmented dermatoscopic images is provided. Almost all related methods follow similar approaches but using different features. We have introduced several new features which could improve the accuracy of diagnosing melanoma. For evaluation we have implemented and tested all methods on the ISIC archive, which is the largest openly available dataset of dermatoscopic melanoma images. Our method outperforms most recent previous works’ accuracy on the ISIC dataset by 1.5 percent. It also achieves a 2.32-point higher F1 score while obtaining a comparable sensitivity.