## 4 Results
### Deep-Learning Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author \& Year** & **Classification Task** & **Dataset** & **Data** & **Methods Used** & **Cross Validation** & **Results** \\ \hline \hline \multirow{4}{*}{**[PERSON] et al. [97]** 2022**} & Binary: & & & & Use of some & & \\  & benign vs. malignant & & & & pre-trained & & \\  & & & & networks. VGG16, & & \\  & 2022 & benign vs. malignant & & & VGG19, & & \\  & & & & DenseNet101 & & & \\  & & & & & and ResNet101 & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [98]**} & Binary: & & & Image rotation: & Use of pre-trained & & \\  & benign vs. malignant & & & 180 degrees: & ResNet52 in & - & \\  & 2021 & & & vertical flipping & 5 different situations & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [99]**} & Binary: & & & Use of VGG16 in & & \\  & benign vs. malignant & & & & 3 different situations & - & \\  & 2017 & & & & & PR = 0.797 \\ \hline \multirow{4}{*}{**[PERSON] et al. [100]** 2020} & Binary: & & & Image rotation: 90, & & & \\  & benign vs. malignant & & 180, 270 degrees: & & & \\  & & & mirroring, center & Custom architecture & & \\  & benign vs. malignant & & cropping, & & based on MobileNet & - & \\  & & & brightness change & and DenseNet & & \\  & & & and random occlusion operations & & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [101]**} & Binary: & & & Affine Image & & & \\  & MM vs. benign & & & Transformation and & ResNet50 & - & \\  & MM vs. benign & & MedNode, ISIC2020 & & & \\  & & MedNode, ISIC2020 & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [102]**} & Binary: & & & & & \\  & benign vs. malignant & & & & CNNs based on & \\  & 2022 & benign vs. malignant & & ISIC2019, ISIC2020 & - & VGG16, ResNet50, & - & \\  & & & DenseNet169 & & & \\  & & & and EfficientNetB3 & & \\ \hline \multirow{4}{*}{**[PERSON] [103]** 2022} & Multi-class: MM, & & & & Use of a & & \\  & benign, malignant & & 7-point & & meta-learning & \\  & & & & method and pre- & & \\  & & & & trained ResNet50 & & \\ \hline \multirow{4}{*}{**[PERSON] [104]**} & Multi-class: MM, & & & Random brightness, & & \\  & BCC, AK, SK, DF, & & & contrast changes, & & \\  & VL, SCC & & & random flipping, & Use of pre-trained MobileNet & \\  & & VL, SCC & & & \\  & & & & and shear, & \\  & & & & and CutOut & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [105]**} & Multi-class: N/AN, & & & Use of three & & \\  & MM, SK, BCC, DF, & & HAMI10000 & Image rotation, & networks & & \\  & 2020 & AK, VL & & zoom, horizontal/ & (EfficientNet, SENet & - & \\  & & AK, VL & & vertical flipping & & \\  & & & & three & \\  & & & & different situations & \\ \hline \multirow{4}{*}{**[PERSON] [106]** 2019} & Multi-class: N/AN, & & & Image rotation, & & \\  & MM, SK, BCC, DF, & & HAMI10000 & Image rotation, & & \\  & MM, SK, BCC, DF, & & & & \\  & & & & correct, pixel jitter, & \\  & & & & Aspect Ratio, & \\  & AK, VL & & random shear, & & \\  & & & zoom, and vertical/horizontal shift & & \\  & & & and flip & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [107]**} & Multi-class: N/AN, & & & Combination of & & \\  & MM, SK, BCC, DF, & & & MoGG16 and & & \\  & AK, VL & & horizontal flipping & & \\  & & & & trained networks & \\ \hline \multirow{4}{*}{**[PERSON] et al. [108]**} & Multi-class: MM vs. & & & & AUC = 0.926, & \\  & N and KS, MM and & & & & on GoogLeNet & \\  & N vs. SK & & & & \\ \hline \multirow{4}{*}{**[PERSON] et al. [109]**} & Multi-class: N/AN, & & & & & \\  & MM, SK, BCC, DF, & & & & \\  & AK, VL & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author \& Year** & **Classification Task** & **Dataset** & **Data** & & & \\ \hline \multirow{2}{*}{**[PERSON] et al. ** [110]} & Binary: benign vs. malignant & \multirow{2}{*}{ISIC archive} & Re-scaling, shearing, vertical/horizontal flipping, zoom & \multirow{2}{*}{**Methods Used**} & \multirow{2}{*}{**Cross Validation**} & \multirow{2}{*}{**Results**} \\  & & & & & & \\ \hline \multirow{2}{*}{**[PERSON] et al.