## 3 Results and Discussion  
The main aim of this research was to develop an efficient and robust method for the detection and classification of melanomas in simple and dermatological images using image processing and a multilayer perceptron. The algorithm estimates the descriptors of the ABCD rule and uses the values as input for the multilayer perceptron. It is responsible for the training and classification of malignant and benign melanoma.  
Figures 9 and 10 illustrate the identification of the contour and the type of melanoma according to the analysis of the results (contour color indicates severity). Severity levels are an additional classification of malignant cases of melanoma.  
Figure 8: Cross-Validation, the 10 subsets are randomly selected.  
Figure 9: Examples of identification and classification of malignant melanoma cases in simple images.  
The validation was divided into two stages; the first was the cross-validation using Equations (4)-(7), and the second was statistical analysis. According to the results, the method has better efficiency than conventional methods since, after applying the equations, an efficiency of 97.78% for simple images and 98.22% for dermatological images was obtained. Table 4 shows the precision values obtained in the iterations for both simple and dermatological images. Figure 11 illustrates the comparison of the efficiency levels of the development method with studies that used the same type of images and datasets.  
The second classification is responsible for categorizing the severity of the malignant melanoma once it has been diagnosed. The maximum and minimum obtained from the estimation of each descriptor are used for the classification. The analysis of the results for the second metric indicates that the levels for cases of malignant melanoma correspond to level 1 when they are less than 0.35 (green color), level two when they are less than 0.65 (yellow color), level three when they are less than 0.85 (orange), and level four when they are greater than 0.85 (red); Figures 9 and 10 show examples of malignant melanoma with different levels of severity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Type of Image** & **k = 1** & **k = 2** & **k = 3** & **k = 4** & **k = 5** & **k = 6** & **k = 7** & **k = 8** & **k = 9** & **k = 10** & **Average** \\ \hline
**Dermatological** & 97.89 & 98.15 & 100 & 97.23 & 100 & 97.9 & 100 & 98.41 & 97.86 & 99.11 & 98.65 \\
**Simple** & 98.27 & 99.01 & 96.89 & 99.30 & 100 & 97.98 & 98.77 & 99.34 & 98.16 & 98.02 & 98.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: 10-fold cross-validation accuracy.  
Figure 11: Comparison of efficiency between reported literature and the proposed approach.  
Figure 10: Examples of identification and classification of malignant melanoma cases in dermatological images.  
Subsequently, the confidence levels of the mean were determined; according to the results, it is said that the average of the cases will fall 97% of the time. It was observed that there is a slight overlap in the ranges of the means. However, the reliability for which it was calculated is high. Thus, it can be said that the range of confidence is high. It is concluded that the means are statistically different among the groups considering a reliability level of 97%.  
In addition to detecting and classifying cases of malignant and benign melanoma, and detecting level severity, the proposed method offers other advantages such as detecting several cases of melanoma in the same image, identifying images that show skin only and cases of melanoma where the image has hair in it. When the image contains more than one melanoma, a diagnosis is provided for each.