Computer-aided detection of Melanoma using geometric features

Melanoma is one type of skin cancer that usually develops from prolonged exposure to UV light. The latter triggers mutations that lead skin cells to multiply rapidly and form malignant tumors. If not cured, Melanoma can result in one's death. Hence, an early detection of this deadly cancer is important to prevent it. Certain lesion characteristics such as Asymmetry, Border, Color and Diameter segmentation (ABCD rule), can indicate the presence of Melanoma. In this work, we investigate the use of geometric features to differentiate between a benign lesion and a malignant one. The k-Nearest Neighbors (k-NN) machine learning algorithm is used to classify 15 lesions based on their ABD features. An accuracy of 89% was obtained on the testing set. The results indicate that this technique may be used to detect Melanoma skin cancer.