## 7 Discussion
### Comparison with the Literature  
The different existing works regarding the melanoma vs. nevi classification task and using ISIC 2016 as the test dataset, described in [19], are reported below and are finally compared with the proposed model. In [73], the authors propose three different scenarios for using the VGG16 network. First, they train the network from scratch, obtaining the least accurate results. Then, they apply the TL method, which turns out to be better than the first method but suffers from the phenomenon of overfitting. Finally, they apply fine-tuning, obtaining the best results. The authors of [30] propose a framework that performs image preprocessing and performs the classification task using a hybrid CNN consisting of three feature extraction blocks whose results are merged to provide the final output. In [74], an approach is proposed in which shape, color, and texture features are extracted from previously segmented skin lesion images and then concatenated to features automatically extracted from a custom CNN. The comparison between the performance achieved by the proposed model and that of works reported in the literature is summarized in Table 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Accuracy** & **Sensitivity** & **Specificity** & **Total** & **Trainable** & **Time per** \\  & & & **Parameters** & **Parameters** & **Epoch** \\ \hline VGG16 & 61.72 & 80.60 & 58.56 & \(\sim\)124 M & \(\sim\)200 k & \(\simeq\)56\({}^{\prime}\) \\ \hline ResNet50 & 53.63 & 84.21 & 48.54 & \(\sim\)3.5 M & \(\sim\)100 k & \(\simeq\)16\({}^{\prime}\) \\ \hline MobileNet & 60.05 & 86.55 & 55.64 & \(\sim\)7 M & \(\sim\)100 k & \(\simeq\)4\({}^{\prime}\) \\ \hline DenseNet121 & 54.46 & 90.64 & 50.78 & \(\sim\)13 M & \(\sim\)165 k & \(\simeq\)12\({}^{\prime}\) \\ \hline DenseNet169 & 59.05 & 88.30 & 54.18 & \(\sim\)24 M & \(\sim\)200 k & \(\simeq\)14\({}^{\prime}\) \\ \hline DenseNet201 & 59.80 & 86.55 & 55.35 & \(\sim\)18.5 M & \(\sim\)200 k & \(\simeq\)19\({}^{\prime}\) \\ \hline Proposed work & 82.50 & 76.61 & 83.61 & \(\sim\)150 k & \(\sim\)150 k & \(\simeq\)4\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of proposed work for the classification of melanoma vs, nevus with common pre-trained networks. Performance values are in percentages. On the 2016 ISIC dataset, the performance of the model is very high and reflects the accuracy and loss curves shown in Section 6 (Figure 6). Both in terms of accuracy and in terms of sensitivity, specificity, and AUC score, the proposed model outperforms the models reported in the literature. Whether this result is due to the fact that, in contrast to [30], the images were not preprocessed; or to the fact that, in contrast to [74], the texture features were extracted from the entire image and not only from the segmented lesion; or again, that both may have contributed to the result is not known for sure. To show the robustness of the proposed work, an external validation was conducted using the online public database Dermatology Information System (DermIS) [75]. This dataset, which contains 1000 dermoscopic images, of which 500 are benign and 500 are malignant, is the largest public dataset after the ISIC archive. Containing a limited number of data, the DermIS dataset is integrated with other public or private datasets, and classification results are reported globally but not for the split datasets, thus preventing comparison with the literature. A paper was identified that reports classification results on the DermIS dataset not combined with other datasets, the comparison of which with the proposed method is shown in Table 10.  
Although the sensitivity of the model proposed in [76] is higher than that obtained in the present work, the specificity is much lower.