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Skin cancer is one of the most frequently en-countered types of cancer in the Western world. According to the Skin Cancer Foundation Statistics, one in every five Americans develops skin cancer during his/her lifetime. Today, the incurability of advanced cutaneous melanoma raises the importance of its early detection. Since the differentiation of early melanoma from other pigmented skin lesions is not a trivial task, even for experienced dermatologists, computer aided diagnosis could become an important tool for reducing the mortality rate of this highly malignant cancer type.In this paper, a computer aided diagnosis system based on machine learning is proposed in order to support the clinical use of optical spectroscopy for skin lesions quantification and classification. The focuses is on a feasibility study of optical spectroscopy as a medical tool for diagnosis. To this end, data acquisition protocols for optical spectroscopy are defined and detailed analysis of feature vectors is performed. Different tech-niques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions.

Feasibility Study of Optical Spectroscopy as a Medical Tool for Diagnosis of Skin Lesions