###### Abstract
Although it accounts for the minority of skin cancer cases, melanoma is the most aggressive form of skin cancer with an increasing incidence rate. It can prove lethal if not diagnosed in time, so it is crucial to detect it early in the process to increase the chance of cure and recovery [11]. The rules currently used by dermatologists to diagnose melanoma are summarized in Table 1.  
One of the main tools for the early diagnosis of melanoma is dermoscopy, a noninvasive and cost-effective technique [17; 18], that has proved useful in reducing the number of presumptive diagnoses that need to be confirmed histologically by skin biopsy [19]. The equipment magnifies up to 10 times over the area of interest and this allows the physician to obtain what would be possible by employing only the naked eye [20], thus facilitating the detection of certain features of the lesions that are essential for diagnosis, such as symmetry, size, broths, and presence and distribution of color features, but also blue-white areas, atypical pigmented networks and globules [21]. It is, however, a complex, time-consuming procedure that shows a strong dependence on the experience and subjectivity of the physician. The issues illustrated above made necessary the development of computer-aided \begin{table}
\begin{tabular}{c c} \hline \hline
**Diagnosis Rules** & **Description** \\ \hline \multirow{4}{*}{**The ABCDE rule [12; 13]**} & It is based on morphological characteristics such as asymmetry (A), irregularity of the edges (B), nonhomogeneous color (C), a diameter size (D) greater than or equal to 6 mm, and evolution (E) understood as temporal changes in size, shape, color, elevation, and the appearance of new symptoms (bleeding, itching, scalp formation) [14]. \\ \hline \multirow{4}{*}{**Seven Point Checklist [15]**} & It is based on the seven main dermoscopic features of melanoma (major criteria: atypical pigment network, blue-whitish veil and atypical vascular pattern; minor criteria: irregular pigmentation, irregular streaks, irregular dots and globules, regression structures) by assigning a score to each of these. \\ \cline{2-3}  & It is based on 11 features, two negative and nine positive, which are assessed as present/absent. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the melanoma diagnosis rules. Figure 1: Principal types of malignant skin cancer (sources: [10] and Dermatology Unit, Department of Clinical Internal Anesthesiologic Cardiovascular Sciences, “La Sapienza” University of Rome). (a) BCC. (b) SCC. (c) MM. diagnostic systems (CAD systems). Those systems involve the steps shown in Figure 2 for analysing and classifying dermoscopic images of skin lesions [22]. Preprocessing is aimed at mitigating artefacts in the images, mainly due to the presence of hair and marker marks on lesions. A typical hair-removal algorithm comprises two steps [23]: hair detection, for which various morphological, thresholding and filtering operations (Gaussian, middle, and median filters) are applied, and hair repair (restoration or \"inpainting\"). The latter, which consists of filling the image space occupied by removed hair, is performed by means of linear interpolation techniques, nonlinear partial differential equations (PDEs), diffusion methods, or exemplar-based methods. There are also well-known hair-removal algorithms, such as DullRazor [24]. Among image-enhancement methods, the most important are color correction or calibration, which recover real colors of a lesion, but there are also illumination-correction, contrast-enhancement, and edge-enhancement techniques. Illumination correction is performed by illumination-reflectance models, Retinex algorithm [25], and bilateral filter or Monte Carlo sampling [26]. For contrast enhancement, the equalization histogram (HE), the equalization of the adaptive histogram (AHE) and a sharp masking are often used together [25]. Finally, for edge enhancement, the Karhunen-Loeve Transform (KLT), also known as the hoteling transform or principal component analysis (PCA), is widely used [27].