## 4 Experimental Results
### Experimental Results for Skin Lesion Detection Using the Decision Fusion Classifier  
To be able to create a more performant system, the weights presented in Table 6 were assigned for each implied CNN, where M--Melanoma, NM--Non-Melanoma.  
The objective of appropriately configuring the weights is to enhance the effectiveness of the decision fusion-oriented system by influencing the ultimate decision made by the classifiers with higher weights. This trend is evident in Table 6, where, among the initial five CNNs, the weight \(\mathrm{w_{NM}}\) surpasses \(\mathrm{w_{M}}\), while for the remaining three CNNs, \(\mathrm{w_{M}}\) takes precedence over \(\mathrm{w_{NM}}\). This indicates that the first five CNNs exhibit a partial inclination toward predicting a Non-Melanoma outcome, whereas the other three display a preference for predicting Melanoma. The confusion matrix on both ISIC 2020 and DermIS databases of the proposed system was generated and presented in Figure 20. Based on these confusion matrices, the F1 score was calculated for each database.  
The performance of our proposed decision fusion classifier is depicted in Figure 20. Notably, this classifier demonstrates remarkable performance in terms of accuracy and F1 score for both the DermIS and ISIC 2020 databases. The outcomes from the four applications  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Convolutional Neural Network** & \(\mathbf{w_{M}}\) & \(\mathbf{w_{NM}}\) \\ \hline DarkNet-53 & 0.8543 & 0.8599 \\ DenseNet-201 & 0.7897 & 0.7945 \\ GoogLeNet & 0.8296 & 0.8331 \\ Inception-V3 & 0.7972 & 0.8011 \\ InceptionResNet-V2 & 0.8020 & 0.8253 \\ ResNet-50 & 0.8274 & 0.8257 \\ ResNet-101 & 0.8070 & 0.7981 \\ Xception & 0.7513 & 0.7494 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Decision Fusion classifier weights.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CNN** & **Database** & **Accuracy** & **F1 Score** \\ \hline
**Google Teachable Machine** & DermIS & 0.7800 & 0.7786 \\  & ISIC & 0.7600 & 0.7597 \\ \hline
**Google Vertex AI** & DermIS & 0.8333 & 0.8331 \\  & ISIC & 0.7400 & 0.7370 \\ \hline
**Microsoft Azure Machine Learning** & DermIS & 0.7967 & 0.7966 \\  & ISIC & 0.8200 & 0.8200 \\ \hline
**SalesForce** & DermIS & 0.8033 & 0.8025 \\
**Einstein** & ISIC & 0.7933 & 0.7900 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results for the decision fusion classifier vs. the automated applications.  
Figure 20: Confusion Matrix for Decision Fusion Classifier.  
are also illustrated in Figure 20 for comparison. Evidently, our proposed system yields superior results compared to those obtained from the four applications. Additionally, it is apparent that, apart from Microsoft Azure Machine Learning, better outcomes were achieved on the ISIC 2020 database. Our proposed method attains an F1-score of 0.955 for the DermIS database and 0.9350 for ISIC 2020. As previously stated within the paper, our perspective is that a system should ideally produce a higher count of FP than FN. This preference stems from the notion that it is more advantageous for both the system and the patient to recommend further investigations than to incorrectly label a melanoma as a benign lesion, which could potentially elevate the risk of patient mortality. Figure 20 illustrates that our proposed system indeed generated a greater number of False Positives in both the ISIC and DermIS datasets.