# A Measurement Software for Professional Training in Early Detection of Melanoma
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**EXAMINER Performance Index**} & \multicolumn{3}{c}{**ED**} & \multicolumn{3}{c}{**NED**} & \multicolumn{1}{c}{**MS**} & \multirow{2}{*}{**Biopsy/ Reference**} \\ \cline{2-2} \cline{5-10}  & **S.C.** & **B.B.** & **M.A.** & **C.C.** & **A.M.** & **F.C.** & **MS** \\ \hline
**Sensitivity \%** & 92 & 86 & 84 & 72 & 66 & 70 & 86 & - \\
[MN\({}_{\text{corr},\text{classif}}\)/MN\({}_{\text{Reference}}\)] & 96 & 94 & 94 & 90 & 91 & 90 & 74 & - \\ \hline
**Specificity \%** & 95 & 92 & 92 & 86 & 85 & 85 & 77 & - \\ \hline
**Diagnostic Accuracy \%** & 94 & 92 & 91 & 86 & 85 & 85 & 77 & - \\ \hline
**Clinical Accuracy \%** & 94 & 92 & 91 & 86 & 85 & 85 & 77 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of comparison of lesion classification results by expert (ED)/non-expert (NED) dermatologist groups and automatic measurement system (MS). the clinic and histopathology [27; 28]. The definition of dermoscopic criteria has led to the establishment of a real \"dermoscopic semeiotic\" in which each ELM criteria correlates to specific morphological and histopathological parameters. However, it has been shown that dermoscopy is a counter-productive tool for diagnostic accuracy when used by a NED [10; 11]. In fact, dermatoscopy remains a highly operator-dependent method, despite the various dermoscopic algorithms (ABCD, Menzies method, Seven-Point Checklist, Three-Point Checklist) developed in order to simplify and standardize the diagnostic procedures to follow during a dermoscopic examination. For this reason, in the last twenty years, an increasing interest in automated analysis of dermoscopic images of pigmented skin lesions has been developed to discriminate between melanoma and other pigmented skin lesions [29]. Several research groups have dedicated time to develop automated diagnostic systems capable of identifying dermoscopic criteria indicating malignancy [30]. Many of these systems are based on the ABCD algorithm. For example, [PERSON] et al. [31] developed an automated color segmentation algorithm used to identify the characteristics of cutaneous tumors. In 1999, [PERSON] and [PERSON] [32; 33] proposed a pattern-based color segmentation without extraction. Two years later, [PERSON] and co. [34] developed an automated recognition system for melanoma, in which 21 different parameters were extracted from the images. More recently, [PERSON] and colleagues [35] introduced a new algorithm based on the comparison by a gray scale of points extracted from the ELM images of pigmented lesions. In [12], an algorithm was described to diagnose pigmented lesions, by taking into account 64 analytical parameters, while a software for automatically evaluating 50 objective parameters of skin lesions (divided into 3 categories: Geometry, texture, and pigmented areas) was proposed in [29]. More than 400 further studies about automated diagnostic systems have been published since 2002: The review in [36] shows that computerized diagnosis systems have more diagnostic sensitivity than diagnostic specificity, which is less than classical dermoscopy practiced by experienced dermatologists, with a total diagnostic performance that can be overlapped. Other reviews about the automated melanoma