EARLY DETECTION OF MELANOMA SKIN CANCER USING CLASSIFIERS

Abstract: Melanoma spreads through metastasis, and therefore it has been proved to be very fatal. A system to prevent this type of skin cancer is being awaited and is highly in-demand. It is important to highlight that excess exposure to radiations from the sun gradually erode melanin in the skin. Moreover, such radiations penetrate into the skin thereby destroying the melanocytes cells. Melanomas are asymmetrical and have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for melanoma early detection and prevention. In this work, the components of a portable real-time noninvasive skin lesion analysis system to assist in the melanoma prevention and early detection are proposed. The first component is a real-time alert to help users to prevent skin burn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis including image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The framework has been developed in a smart-phone application. The experimental results show that the proposed system is efficient, achieving high classification accuracies.

Keywords: Image Segmentation, Skin Cancer, Melanoma.