An algorithm for characterizing skin moles using image processing and machine learning

Skin cancer is becoming more frequent in recent years due to increased solar radiation, with the elderly being the most affected. Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. In many parts of the world, there are low-income regions that lack specialist dermatologists, and skin cancer is diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as Support Vector Machine (SVM), Random Forest and Naive Bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles.