# Skin Lesion Classification Using a Deep Ensemble Model
## 4 Results  
This section evaluates the performance of the proposed deep ensemble model against the individual models, VGG16, ResNet-50, and Inception-V3, focusing on accuracy metrics. In this system, the ISIC dataset from Kaggle is employed, specifically targeting images depicting the classification of BCC, SCC, and melanoma. At the outset, the system's performance is evaluated using the original dataset, which is inherently unbalanced. Subsequently, the evaluation process is repeated using a balanced dataset, which is achieved through the application of oversampling techniques. These models are created and trained using Keras, which runs on the TensorFlow framework. For thorough evaluation, the dataset is split into training and testing sets with a 75-25% ratio. Specifically, 75% of the data are randomly chosen for training, while the remaining 25% are set aside for testing. Table 1 describes the parameters of VGG16, ResNet-50, and Inception-V3. Figure 6 presents the comparative performance results of the VGG16 model on both the original unbalanced dataset and the dataset balanced through oversampling. Figure 7 describes the comparative performance results of the ResNet-50 model on both the original unbalanced dataset and the dataset balanced through oversampling. Figure 8 depicts the comparative performance outcomes of the Inception-V3 model on both the initial unbalanced dataset and the dataset balanced through oversampling. Similarly, Figure 9 showcases the comparative performance outcomes of the deep ensemble model on the original dataset and the dataset balanced through oversampling. Table 2 describes the confusion matrix for each class. Table 3 presents the sensitivity and specificity of each class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **VGG16** & **ResNet-50** & **Inception-V3** \\ \hline Input Image size & 224 \(\times\) 224 & 224 \(\times\) 224 & 299 \(\times\) 299 \\ \hline Batch Size & 32 & 32 & 32 \\ \hline Learning Rate & 0.001 & 0.001 & 0.001 \\ \hline Optimizer & Adam & Adam & Adam \\ \hline Epochs & 150 & 150 & 150 \\ \hline Loss Function & Categorical Crossentropy & Categorical Crossentropy & Categorical Crossentropy \\ \hline Dropout Rate & 0.5 & 0.5 & 0.5 \\ \hline Data Augmentation & Yes & Yes & Yes \\ \hline Training/Validation Split & 75\%/25\% & 75\%/25\% & 75\%/25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings of three models. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Predicted BCC** & **Predicted SCC** & **Predicted Melanoma** \\ \hline Actual BCC & 320 & 30 & 26 \\ Actual SCC & 15 & 150 & 16 \\ Actual Melanoma & 20 & 25 & 393 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for each class. Figure 6: Results of VGG16 model on unbalanced and balanced datasets. Figure 7: Results of ResNet-50 model on unbalanced and balanced datasets. Figure 8: Results of Inception-V3 model on unbalanced and balanced datasets. Figure 9: Results of deep ensemble model on unbalanced and balanced datasets. Figure 10 presents the comparative accuracy results using the original unbalanced dataset. Figure 11 describes the comparative accuracy results using the dataset balanced through oversampling. The performance evaluation of the skin lesion classification system reveals that the deep ensemble model comprising VGG16, ResNet-50, and Inception-V3 significantly outperforms each individual model on both the original ISIC dataset and the balanced ISIC dataset achieved through oversampling. For the original dataset, the ensemble model achieves an accuracy of 91%, compared to 83% for VGG16, 86% for ResNet-50, and 88% for Inception-V3. On the balanced dataset, the ensemble model further improves with an accuracy of 97%, demonstrating its robustness in handling class imbalance.