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

Skin cancer is one of the most frequently encountered 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 techniques for supervised and unsupervised learning are explored on clinical data, collected from patients with malignant and benign skin lesions. Keywords—Melanoma; Classification; Supervised Learning; Computer–Aided Diagnosis; Machine Learning; Optical Spectroscopy