# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 8 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref** & **Method** & **Number of Classes** & **Accuracy** & **Precision\%** & **Recall\%** \\ \hline
[18] & VGGNet & Two & 81.3 & 79.74 & 78.66 \\ \hline
[47] & Fully Convolutional Network & Five & 85.8 & N/A & N/A \\  & Ten & 81.8 & & \\ \hline
[32] & Multi-tract CNN & Ten & 79.15 & NA & N/A \\ \hline
[11] & CNN & Three & 69.14 & N/A & N/A \\  & CNN-PA & Nine & 72.1 & & \\  & CNN & & 48.9 & & \\  & CNN-PA & & 55.4 & & \\ \hline
[20] & VGG16 & Seven & 75.6 & N/A & N/A \\  & ResNet50 & & 86.6 & & \\  & DenseNet121 & & 89.2 & & \\  & Xception & & 90.1 & & \\  & Inception V3 & & 74.3 & & \\  & DenseNet161 & & 88.7 & & \\ \hline
[19] & VGG16 & Seven & 80.1 & N/A & N/A \\  & GoogleNet & & 79.7 & & \\ \hline
[49] & ResNet50 & Seven & 87.1 & 78.6 & 77.0 \\  & InceptionV3 & & 89.7 & 84.9 & 80.0 \\ \hline
[56] & MObileNet & Seven & 83.1 & 89.0 & 83.0 \\ \hline
[19] & InceptionResNetV2 & Seven & 70.0 & N/A & N/A \\  & PNASNet-5-Large & & 76.0 & & \\  & SENet154 & & 74.0 & & \\  & InceptionV4 & & 67.0 & & \\ \hline
[57] & Triple-Net+CAM-BP & Two & 82.0 & N/A & N/A \\ \hline
[13] & Dilated VGG 16 & Seven & 87.42 & 87.0 & 87.0 \\  & Dilated VGG 19 & & 85.02 & 85.0 & 85.0 \\  & Dilated MobileNet & & 88.22 & 89.0 & 88.0 \\  & Dilated InceptionV3 & & 89.81 & 89.0 & 89.0 \\ \hline
[10] & IRRCNN & Seven & 87.0 & N/A & N/A \\ \hline
[12] & CNN & Seven & 77.0 & N/a & N/A \\  & CNN (one vs All) & & 92.90 & & \\ \hline
[31] & InceptionV3 & Seven & 91.56 & 89.0 & 89.0 \\  & ResNetXt101 & & 93.20 & 88.0 & 88.0 \\  & InceptionResNetV2 & & 93.20 & 87.0 & 88.0 \\  & Xception & & 91.47 & 89.0 & 88.0 \\  & NASNetLarg & & 91.11 & 86.0 & 86.0 \\ \hline Proposed & ResNet & Eight & 92.0 & 0.92 & 0.92 \\  & InceptionV3 & & 72.0 & 0.79 & 0.65 \\  & DenseNet & & 92.0 & 0.93 & 0.91 \\  & InceptionResNetV2 & & 91.0 & 0.93 & 0.92 \\  & VGG 19 & & 91.0 & 0.91 & 0.92 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with other deep learning-based models. Table 5 shows the performance comparison of the proposed ensemble model with the recent deep learning-based ensemble models proposed in [18; 19; 21; 31; 49]. It is observed from the table that the majority voting, weighted averaging, and weighted majority ensemble models have an accuracy of 98%, 98.2%, and 98.6%, respectively, which is much higher than the ensemble models proposed in [18; 19; 21; 31; 49]. It is observed from the literature that in classification, as the number of classes increases, the classification accuracy decreases. The previous works carried out in [18; 19; 31; 49] have lower accuracy as compared to the proposed ensemble models. Our ensemble models have outperformed both the dermatologists and the recently developed deep learning-based models for multiclass skin cancer classification without extensive pre-processing. Figure 6 shows the training accuracy of individual deep learning models. Confusion matrices of individual and ensemble models are shown in Figure 7. The motivation for adopting the ensemble learning models is that they improve the generalization of the learning systems. Machine learning models are bounded by the hypothetical spaces that have bias and variance. The ensemble models combine the decision of individual weak learners to overcome the problem of the single learner that may have a limited capacity to capture the distribution (causing variance error) present in the data. Our results show that making a final decision by consulting multiple diverse learners may help in improving the  
Figure 6: Training and validation accuracy vs. loss.