# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 3 Materials and Methods
### Methods  
The application of transfer learning has assisted significantly to resolve the general problem of insufficient data for the training of machine learning models [72]. The overarching goal was to ensure the realization of a learning algorithm that can accurately discriminate skin lesions in dermoscopic images. The investigated base learners were subjected to a two-stage process to determine the relevance of an image segmentation process. It was also to examine the discriminating prowess of the base learners for constructing novel ensemble deep learning algorithms. There are several approaches such as bagging, boosting, stacking, and voting previously used in the literature to improve the classification performance of ensemble learning. The approach of voting ensemble learning that follows the application of heterogeneous learners with multiclass labels was explored in this study to enhance the generalization of skin lesion classification. The impetus for the exploration of this approach includes its wide usage, simplicity, and efficiency [73; 74]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multicolumn{5}{c}{**Benign**} & \multicolumn{5}{c}{**Malignant**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{5-10}  & **AKIEC** & & **BKL** & & **DF** & **INDB** & **NV** & **SK** & **VASC** & **BCC** & **MEL** & **SCC** \\ \hline ISIC\_2017\_TRN [4] & & & & & 1372 & 254 & & & 374 & & 2000 \\ \hline ISIC\_2017\_VAL [4] & & & & & 78 & 42 & & & 30 & & 150 \\ \hline ISIC\_2016\_TRN [66] & & 1 & & 29 & 688 & & & & 182 & & 900 \\ \hline ISIC\_2019\_TRN [4; 67; 69] & 867 & 2624 & 239 & & 12,875 & & 253 & 3323 & 4522 & 628 & 25,331 \\ \hline ISIC\_2018\_TRN [67; 68] & 327 & 1099 & 115 & & 6705 & & 142 & 514 & 1113 & & 10,015 \\ \hline ISIC\_2020\_TRN [70] & & & & 27,124 & 5193 & 135 & & & 584 & & 33,036 \\ \hline Total & 1194 & 3724 & 354 & 27,153 & 26,911 & 431 & 395 & 3837 & 6805 & 628 & \\ \hline Duplicates & 327 & 1396 & 115 & & 8843 & & 142 & 514 & 1687 & & \\ \hline Discarded & & & & 29 & & & & 12 & & \\ \hline Final Selection & 867 & 2328 & 239 & 27,124 & 18,068 & 431 & 253 & 3323 & 5106 & 628 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental datasets for training and validation. Figure 2: Samples of Skin Lesion Images. The methods of this study were based on the VGGNet with 11, 13, 16, and 19 layers, ResNet with 18, 34, 50, 101, and 152 layers, ResNeXt having 50, and 101 layers, DenseNet with 121, 161, 169, and 201 layers, DPN with 68, 92, 98, 107, and 131 layers, ECA ResNet of 101 layers, IG ResNeXt having 101 layers, SWSL ResNeXt of 101 layers, ReXNet with \(x1.5\) and \(x2.0\) repeated layers. The VGGNet uses multiple non-linear rectification layers instead of a single rectification layer for a more discriminative result. The application of VGG-11, VGG-13, VGG-16, and VGG-19 networks was examined in this study [56]. ResNet applies an identity shortcut connection to solve the problem of vanishing gradient resulting from a deeper network [57]. The network uses a pre-activation residual block so that both forward and backward signals are directly propagated from one block to any other block with identity mapping. This mechanism has helped the use of an additive merging of previous layers into the future layers where the network can learn residuals that are the differences between the previous and current layers. The application of ResNet-18, ResNet-34, ResNet-50, ResNet-101 and ResNet-152 with other recent variations of ResNet was examined in this study. The evaluated variations of the network include residual networks with next dimension (ResNeXt-50, ResNeXt-101) [58], IG-ResNeXt-101 [62], SWSL-ResNeXt-101 [63], and efficient channel attention-based residual networks (ECA-ResNet-101) [61]. DenseNet is a logical extension of ResNet that concatenates the outputs from the previous layers into future layers, instead of using additive merges to sum residuals [58].