## 5 Discussion and Conclusions  
Skin cancer is one of the most common cancers in the world with a high mortality rate. Early identification and diagnosis of skin lesions is essential to determine the best treatment for the patient and to increase the survival rate in the case of cancerous lesions. Diagnosis of this disease is conducted manually by more or less experienced dermatologists, but it proves to be time consuming and difficult. By using CAD systems, this procedure can become much easier, faster, and more accurate. This systematic literature review aims to provide an overview of the use of machine learning and deep learning in dermatology to help future researchers. Scientific publications published between 2012 and 2022 related to ML and DL approaches for the detection and classification of skin lesions were selected. The searches, conducted in the arXiv and Science Direct databases, resulted in the selection of 68 research articles that focused on skin lesion classification using images from public datasets and reported the results obtained in terms of model performance. Having chosen the use of public datasets among the inclusion criteria, there are no papers prior to 2016. Furthermore, more than half of the articles on ML were published from 2020 to 2022, and more than half of the articles on DL and ML/DL were published from 2021 to date. Overall, 70% of the papers selected for this article have been published in the past two years. The use of public datasets for model training and validation allows comparison of work, a key point of scientific research. An analysis of the datasets used in the papers cited in this review article shows that the HAM10000 dataset and the ISIC archive are the most frequently chosen datasets for training and testing skin lesion classification models (Figure 4). Of the latter, moreover, the 2016 and 2017 versions are the most frequently used over the past decade. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author \& Year** & **Classification Task** & **Dataset** & **Data Augmentation** & **Methods Used** & **Cross Validation** & **Results** \\ \hline
**[PERSON] et al. [137]** & Binary: benign vs. malignant & ISIC archive, ISIC2020 & - & Ensemble of six CNN + SVM &
\begin{tabular}{c} \(\text{F1}=0.23\pm 0.04\), \\ AUC-PR = \\ 0.16 + 0.04, \\ AUC = 0.87 + 0.02 \\ \end{tabular} \\ \hline
**[PERSON] et al. [138]** & Binary: MM vs. non-MM & ISIC2016, ISIC2017, HAM10000, PH\({}^{2}\) & \
\begin{tabular}{c} \(\text{F2}_{X}\) using \\ pre-trained \\ DenseNet121 \\ network + MPL \\ \end{tabular} & 5-fold &
\begin{tabular}{c} \(\text{F1}=0.23\pm 0.04\), \\ AUC-PR = \\ 0.16 + 0.04, \\ AUC = 0.87 + 0.02 \\ \end{tabular} \\ \hline
**[PERSON] et al. [139]** & Binary: MM vs. SK & ISIC2016 & - & \
\begin{tabular}{c} \(\text{F2}_{X}\) using \\ pre-trained AlexNet, \\ VGG16, ResNet18 \\ and ResNet101 \\ models + RBF-SVM \\ \end{tabular} & - &
\begin{tabular}{c} \(\text{MMSE}=0.812\), \\ SP -0.785, \\ AUC = 0.873,SK \\ \end{tabular} \\ \hline
**[PERSON] et al. [140]** & Binary: MM vs. non-MM; SK vs. non-SK & ISIC2017 & - & \
\begin{tabular}{c} \(\text{F2}_{X}\) using \\ pre-trained \\ ResNet50 and \\ DenseNet201 \\ models + RBF-SVM \\ \end{tabular} & - &
\begin{tabular}{c} ResNet: ACC = 0.87, \\ AUC = 0.89; \\ DenseNet \\ AUC = 0.87, \\ AUC = 0.89 \\ \end{tabular} \\ \hline
**[PERSON] et al. [141]** & Binary: MM vs. others; SK vs. others & Image rotation, flipping, translation, and cropping color-based data augmentation & \
\begin{tabular}{c} \(\text{F}_{EX}\) from CNNs + \\ linear SVM \\ \end{tabular} & - &
\begin{tabular}{c} \(\text{MMSE}=0.812\), \\ SP -0.875, \\ AUC = 0.84, \\ AUC = 0.84,SK \\ AUC = 0.92, \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._The research conducted shows that the most widely used ML classifier is the SVM model (Figure 5), while pretrained convolutional neural networks account for the majority of DL and ML/DL approaches (Figure 6), and that among the many solutions identified, those based on DL represent the majority.