A Thermal-Imaging System and Machine-Learning Classification Algorithm for Skin Cancer Screening

Abstract

Skin cancer affects over 2 million people worldwide each year. Although dermoscopy is the gold standard screening technique, it only assesses the superficial features of skin lesions. Novel approaches based on thermal investigation have revealed a correlation between thermal recovery and vascular pattern alterations, which is an important factor in discriminating malignant and benign lesions. In this study, a dynamic thermal-imaging system was designed, developed, and validated in a real clinical scenario. The system is non-invasive, compact, and cost-effective, comprising a cooling probe and an image acquisition system equipped with RGB and thermal cameras. The system incorporates a machine-learning classification algorithm for skin cancer screening. The system showed an accuracy of 89.7% in distinguishing between malignant and benign lesions in a case study involving 58 patients and classified sub-classes of lesions (i.e., melanoma and nevi) with an accuracy of 95.5%. These findings underscore the potential benefit of the proposed dynamic thermal-imaging system as a support tool for non-invasive screening and early detection of malignant skin lesions.