# A Measurement Software for Professional Training in Early Detection of Melanoma
## 1 Introduction  
One of the leading cancers around the world is represented by the malignant melanoma, whose dramatic number of diagnoses seems to be also influenced by both the change in recreational behavior and increasing ultraviolet radiation. According to the World Health Organization (WHO), there are more new cases of skin cancer than the combined incidence of cancers of the breast, prostate, lung, and colon [1]. In detail, melanoma is characterized by a biphasic growth which, at the beginning, evolves \"horizontally\", forming a stratified lesion above the basement membrane (intra-epidermalphase, without metastatic potential), and in a second time, increases \"vertically\" by penetrating deep into the dermis (invasive phase with high metastatic risk). Retrospective epidemiological studies have shown that if the melanoma is diagnosed early, with a thickness less than 1 millimeter (thin melanoma), the survival rate approaches 100% of cases (investigations have shown that the curability rate of thin melanomas is between 91.8% and 98.1% [2]). For this reason, preventive and screening campaigns for increasing public awareness and improving the early diagnosis of melanoma by identifying its precursors and early warning signs are today becoming more important [3]. One of the most useful diagnostic tools in the identification of atypical melanocytic lesions is dermoscopy (also known as epiluminescence microscopy (ELM) [4]. In detail, dermoscopy is a noninvasive technique that allows the morphological features of skin layers to be identified by suitable magnification and lighting systems [5; 6]. With respect to the clinical inspection by the naked eye, the adoption of dermoscopy allows the diagnosis accuracy of pigmented skin lesions to be improved from 10% to 27% [7]. The images seen through a dermatoscope are, therefore, very different from both the clinical and the histopathological images. Dermoscopy is an ideal interface between these two areas. The major limitation of this method is its dependence on the experience of the observer [8]. The sensitivity of diagnostic accuracy of dermoscopy is closely related to the specific experience of the investigator and may be significantly lower in observers who are not formally trained. Not surprisingly, [PERSON] et al. [9] have shown that the patterns of analysis of dermoscopic images on one hand improves the diagnostic performance of experienced dermatologists, but on the other, worsens that of non-expert ones. These data were largely confirmed by a more recent review [10]. In order to overcome this problem, new diagnostic strategies have been proposed with the aim to ensure greater \"objectivity\" in the early detection of suspicious melanocytic lesions, especially for dermatologists not so experienced in dermoscopy. Among these, the introduction of digital systems for the acquisition and analysis of images represents an additional useful tool for the early diagnosis of melanoma. With these methods, it is possible to analyze images using defined algorithms that process the dermoscopic images, identifying features that can discriminate melanoma from other pigmented skin lesions. Dermatologists expert in dermoscopy have a greater diagnostic sensitivity and specificity compared to non-expert ones [11]. This is a very promising field of non-invasive diagnostics, although not without its difficulties, especially arising from the complexity of design and application in daily clinical practice. Several studies have evaluated and demonstrated the possibility to reach, with these systems, diagnostic accuracy levels similar to those of experienced dermatologists [12]. Until now, the data obtained in these studies, based on clinical diagnoses made in experimental conditions, should be interpreted with caution and, in our opinion, are not sufficient to demonstrate the ability of any automated diagnostic system to replace the diagnostic capacity of the specialist's eye. Instead, they are a valuable support to the diagnosis, based on validated and widely accepted dermoscopic algorithms.