# Skin Cancer Disease Detection Using Transfer Learning Technique
## 4 Results and Discussion
### Proposed Model Performance on ISIC-2020 Dataset  
The experiment was conducted to assess the performance of the introduced MobileNetV2 architecture. In the experiment, the Adam optimizer, binary-cross-entropy loss function, 100 epochs, 64 batch size, and the default alpha rate were used as shown in Table 4. The experimental outcomes presented that the introduced method obtained 98.1% and 98.4% accuracy for melanoma and benign lesions, respectively. It also obtained 98.2% average accuracy on the ISIC-2020 dataset, as presented in Table 5. The transfer learning model achieved 98.3% and 98.0% recall on melanoma and benign skin cancer. It obtained a 98.1% F1-score on both diseases, and 98.0%, 98.3% precision on melanoma and benign diseases, respectively. The ISIC 2020 test (leader board) results showed that the proposed method obtained 98.04%, which means there was not enough of a difference between the two test accuracies, our test set and the leader board test set, as shown in Table 5. There were 10,982 images in the ISIC 2020 test set on the leader board, with 690 unique patient IDsand 10,292 duplicates. Because the ground truth for ISIC 2020 was not publicly available, the organizer's statistic on Kaggle area under the receiver operating characteristics curve (AUC) was used. The accuracy and losses in every epoch during training and validation are shown in Figure 6. It shows that after 10 epochs, the training and validation accuracies increased rapidly, and it was steady after almost 40 epochs. On the other hand, training and validation losses decreased rapidly after 10 epochs, and after 60 epochs, the losses of training and validation became stable. The results demonstrated that the proposed method yielded higher classification scores on the ISIC-2020 dataset when the data augmentation strategies were used in the training set. The confusion matrix is a valuable ML method that determines the recall, accuracy, ROC curve, and precision of a model. A confusion matrix was used to measure the classification accuracy visually. It indicated the greater classification accuracy of the MobileNetV2 of the appropriate class in a dark color, whereas a lighter color indicated the incorrectly identified samples. Correct predictions were displayed diagonally in the confusion matrix, whereas incorrect predictions were displayed off-diagonally in the confusion matrix. As demonstrated by the results, the presented MobileNetV2 framework outperformed when data augmentation methods were implemented in the ISIC-2020 dataset, as indicated in Figure 7. It indicated that the MobileNetV2 model correctly identified 1721 benign lesion images out of 1750 and 1556 malignant images out of 1750. The overall accuracy of the presented MobileNetV2 system was 98.2%, and 1.8% error, which indicated the introduced MobileNetV2 model's generalization. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Melanoma** & **Benign** & \
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Leader Board** \\ **Accuracy** \\ \end{tabular} \\ \hline Accuracy & 98.1\% & 98.4\% & 98.2\% & 98.04\% \\ \hline Recall & 98.3\% & 98.0\% & - & - \\ \hline F1-Score & 98.1\% & 98.1\% & - & - \\ \hline Precision & 98.0\% & 98.3\% & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracies, recall, precision and F1-score of presented MobileNetV2 Model on ISIC-2020 dataset. Figure 6: (**a**) Accuracies graph, and (**b**) loss graph of the proposed model. The MobileNetV2 model demonstrated outstanding classification performance in validation and test set classes by having a larger area under the curve (almost 98.2%). The developed methodology's performance was measured using the ROC curve as depicted in Figure 8. The black indicated the ROC curve, and the red indicated the random guessing.