Abstract
Traditional methods for early detection of melanoma rely upon a dermatologist who visually analyzes skin lesion using the so called ABCDE (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria even though conclusive confirmation is obtained through biopsy performed by pathologist. The proposed method shows a bio-inspired feed-forward automatic pipeline based on morphological analysis and evaluation of skin lesion dermoscopy image. Preliminary segmentation and pre-processing of dermoscopy image by SC-Cellular Neural Networks is performed in order to get ad-hoc gray-level skin lesion image in which we compute analytic innovative hand-crafted image features for oncological risks assessment. At the end, pre-trained Levenberg-Marquardt Neural Network is used to perform ad-hoc clustering of such hand-crafted image features in order to get an efficient nevus discrimination (benign against melanoma) as well as a numerical array to be used for follow-up rate definition and assessment.
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