# 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}{**NED**} & \multicolumn{3}{c}{**NED + MS**} & \multicolumn{1}{c}{**MS**} & \multirow{2}{*}{**Biopsy/ Reference**} \\  & **C.C.** & **A.M.** & **E.C.** & **C.C.** & **A.M.** & **E.C.** & **MS** & **Reference** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Correct (/classified)** \\ **melanoma (MN)** \\ \end{tabular} } & \multirow{2}{*}{46/67} & \multirow{2}{*}{42/61} & \multirow{2}{*}{45/66} & \multirow{2}{*}{55/77} & \multirow{2}{*}{53/75} & \multirow{2}{*}{54/76} & \multirow{2}{*}{55/109} & \multirow{2}{*}{64} \\  & & & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Correct (/classified)** \\ **atypical nevus (AN)** \\ \end{tabular} } & \multirow{2}{*}{53/63} & \multirow{2}{*}{55/64} & \multirow{2}{*}{53/61} & \multirow{2}{*}{52/58} & \multirow{2}{*}{55/58} & \multirow{2}{*}{53/57} & \multirow{2}{*}{46/52} & \multirow{2}{*}{73} \\  & & & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Correct (/classified)** \\ **benign nevus (BN)** \\ \end{tabular} } & \multirow{2}{*}{25/30} & \multirow{2}{*}{26/35} & \multirow{2}{*}{25/33} & \multirow{2}{*}{24/27} & \multirow{2}{*}{26/31} & \multirow{2}{*}{24/30} & \multirow{2}{*}{22/24} & \multirow{2}{*}{26} \\  & & & & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Correct (/classified)** \\ **benign nevus (BN)** \\ \end{tabular} } & \multirow{2}{*}{107/110} & \multirow{2}{*}{106/110} & \multirow{2}{*}{107/110} & \multirow{2}{*}{107/108} & \multirow{2}{*}{106/106} & \multirow{2}{*}{106/107} & \multirow{2}{*}{84/85} & \multirow{2}{*}{107} \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of lesion classification by non-expert (NED) dermatologist group and semi-automatic approach (NED + MS). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**EXAMINER Performance Index**} & \multicolumn{3}{c}{**NED**} & \multicolumn{3}{c}{**NED + MS**} & \multirow{2}{*}{**MS**} & \multirow{2}{*}{**Biopsy/ Reference**} \\  & **C.C.** & **A.M.** & **F.C.** & **C.C.** & **A.M.** & **F.C.** & **MS** & **Reference** \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Sensitivity \%** \\ **[MN\({}_{\text{corr\_classif}}\)/MN\({}_{\text{Reference }}\)]** \\ \end{tabular} } & \multirow{2}{*}{72} & \multirow{2}{*}{66} & \multirow{2}{*}{70} & \multirow{2}{*}{86} & \multirow{2}{*}{83} & \multirow{2}{*}{84} & \multirow{2}{*}{86} & \multirow{2}{*}{-} \\  & & & & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **Specificity \%** \\ **[(AN + SN + BN)\({}_{\text{corr\_classif}}\)/(data - MN\({}_{\text{Reference}}\)]** \\ \end{tabular} } & \multirow{2}{*}{90} & \multirow{2}{*}{91} & \multirow{2}{*}{90} & \multirow{2}{*}{89} & \multirow{2}{*}{91} & \multirow{2}{*}{89} & \multirow{2}{*}{74} & \multirow{2}{*}{-} \\  & & & & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Diagnostic Accuracy \%** \\ **Clinical Accuracy \%** \\ \end{tabular} } & \multirow{2}{*}{86} & \multirow{2}{*}{85} & \multirow{2}{*}{85} & \multirow{2}{*}{88} & \multirow{2}{*}{89} & \multirow{2}{*}{88} & \multirow{2}{*}{77} & \multirow{2}{*}{-} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison of lesion classification by non-expert (NED) dermatologist group and semi-automatic approach (NED + MS). diagnosis were published in 2011 and 2012 [37; 38], and confirmed the high diagnostic accuracy and remarkable potential application of them in the clinical field. No previously published study has analyzed the application of automated systems to the Seven-Point Checklist diagnostic algorithm. The Seven-Point Checklist is still one of the most widely used algorithms in the clinical field [39] for the early detection of melanoma. We preferred using the classic Seven-Point Checklist to the revised one, for higher sensitivity. Thus, the classic Seven-Point Checklist would be more suitable processed by an automated software, which by itself tends to over-detect.