# A Measurement Software for Professional Training in Early Detection of Melanoma
## 3 Results  
A database including 270 ELM images of skin lesions and corresponding information (such as patient age and sex, lesion position, histology after excision) was collected during the continuous screening campaign by the Section of Dermatology, University Federico II of Naples, Italy, under the two-year tele-medicine project, \"_Di che segno e il tuo neo?_\" (2018-2019), which involved more than 200 patients and 25 local general practitioners. On the basis of a-posteriori analysis (biopsy) the dermoscopic database includes:  
* 107 benign nevi;
* 99 atypical nevi (73 suspicious nevi);
* 64 melanomas. The corresponding digital images were acquired through iPhone 6/7 smartphones furnished with two dermoscope models (DermLite DL1 and MetaOptima Molescope I): Dimensions of the JPEG color images range from 700 \(\times\) 447 to 2272 \(\times\) 1520 pixels. Aiming to estimate the performance of the proposed measurement and classification system, two groups of physicians were selected:  
* The expert dermatologists (ED) group including 3 physicians (B.B., S.C., M.A.) with clinical activity \(<\)10 years;
* The non-expert dermatologists (NED) group including 3 physicians (C.C., A.M., F.C.) with less than 3 years' experience in dermoscopy;  
and the diagnosis, as well as the application of the Seven-Point Checklist method to each record of the database, were requested to the single dermatologist. In detail, both the diagnostic and clinical accuracy have been evaluated by weighting the corresponding computation according to different class dimensions:  
Figure 8: Application of the classification scheme for the clinical decision. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Weighted TOTAL Score** & **Lesion Classification** & **Clinical Decision** \\ \hline \(T_{S,\mu}<1.5\) & Benign nevus & No excision \\ \(1.5\ \leq\ T_{S,\mu}\ <2.0\) & Suspicious atypical nevus & 6-month follow-up \\ \(2.0\ \leq\ T_{S,\mu}\ <3.0\) & Atypical nevus & Excision \\ \(T_{S,\mu}>3.0\) & Melanoma & Excision \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Automatic classification according to the weighted scoring system. * The former performance represents the weighted mean of sensitivity (i.e., the ratio of the correct detection of melanoma and the corresponding class dimension, 64) and specificity (i.e., the ratio of the correct detection of the benign/atypical nevi and the number of lesions not classified as melanoma, 206);
* The latter performance takes into account the correct decision of both excisions (with respect to the group of lesions including melanomas and strongly atypical nevi) and of following-up/ignoring (with respect to the group of benign and suspicious nevi). The automatic measurement and classification system (MS) has been implemented as Matlab routines at the different stages (image pre-processing, contour extraction, measurement of low-level feature, detection of dermoscopic structures, lesion classification) and executed as distributed code (jobs with multiple parallel tasks) on 64-bit Microsoft Windows computer, featured with Intel Core i7 CPU at 2.67 GHz and 6 GB RAM. To evaluate the quality of the developed automatic system, the reference detection for the dermoscopic structures of each image was achieved by matching the biopsy results with the Seven-Point Checklist filed by the expert dermatologist (S.C.) who exhibited the best diagnostic and clinical accuracy. In detail, every dermoscopic structure should be detected if the corresponding lesion belongs to either a melanoma or strongly atypical nevus group. The authors first evaluated the diagnostic concordance between the two dermatologist groups with different degrees of dermoscopy experience and the MS. Then, they considered the same parameters to evaluate the diagnostic performance of NED when helped by the MS during the dermoscopic diagnosis.