## 4 Experimental Results
### Experimental Results for Skin Lesion Detection Using the Automated Applications  
For model creation, we utilized the built-in interface provided by each application, creating the dataset and training the model. The outcomes of the testing phase are depicted in Figure 19. As illustrated in Figure 19, for the ISIC 2020 database, the most remarkable accuracy and F1 score were achieved using Microsoft Azure Machine Learning. On the other hand, for the DermIS database, the highest accuracy was observed with Google Vertex AI. Delving into the comparison between FP and FN, Google Teachable Machine yielded the highest number of false positives for the DermIS database, while for the ISIC 2020 database, Google Vertex AI recorded the highest count of false positives. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Image**} & \multicolumn{3}{c}{**Im1**} & \multicolumn{3}{c}{**Im2**} & \multicolumn{3}{c}{**Im3**} \\ \cline{2-11} \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Image**} & \multicolumn{3}{c}{**Image**} & \multicolumn{3}{c}{**Image**} & \multicolumn{3}{c}{**Image**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Ground Truth**} & \multicolumn{3}{c}{**NM**} & \multicolumn{3}{c}{**M**} & \multicolumn{3}{c}{**NM**} \\ \hline
**CNN** & \(w_{i}^{M}\) & \(w_{i}^{NM}\) & \(D_{i}\) & \(d_{i}^{M}\) & \(d_{i}^{NM}\) & \(D_{i}\) & \(d_{i}^{M}\) & \(d_{i}^{NM}\) & \(D_{i}\) & \(d_{i}^{M}\) & \(d_{i}^{NM}\) \\ \hline
**CNN\({}_{\mathbf{1}}\)** & 0.8543 & 0.8599 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{2}}\)** & 0.7897 & 0.7945 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{3}}\)** & 0.8296 & 0.8331 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{4}}\)** & 0.7972 & 0.8011 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{5}}\)** & 0.8020 & 0.8253 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{6}}\)** & 0.8274 & 0.8257 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{7}}\)** & 0.8070 & 0.7981 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\
**CNN\({}_{\mathbf{8}}\)** & 0.7513 & 0.7494 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\ \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{\(D_{\mathbf{M}}\)} & \multicolumn{3}{c}{3.2734} & \multicolumn{3}{c}{6.4585} & \multicolumn{3}{c}{0.802} \\ \multicolumn{1}{c}{} & \multicolumn{3}{c}{\(D_{\mathbf{NM}}\)} & \multicolumn{3}{c}{3.1817} & \multicolumn{3}{c}{0} & \multicolumn{3}{c}{6.4871} \\ \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Decision**} & \multicolumn{3}{c}{M} & \multicolumn{3}{c}{M} & \multicolumn{3}{c}{NM} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction of the used CNNs in the testing phase on the ISIC database. Figure 19: Confusion matrices for considered automated applications. (**A–D**): DermIS database, (**E–H**): ISIC database. The primary aim of evaluating the performance of different machine learning models and platforms, such as Google Teachable Machine, Google Vertex AI, Salesforce Einstein Vision, and Microsoft Azure Machine Learning, against a custom classifier that combines these CNNs' decisions, is to determine the most appropriate option for integrating into an EHR system equipped with an automated melanoma detection module. This assessment is a pivotal stage in EHR system development and aids in well-informed decision-making during the planning and solution analysis phases. The main goal is to appraise the accuracy and dependability of each model and platform in accurately identifying cases of melanoma. By considering the combined decisions of various classifiers, decision fusion plays a role in reducing false positives and false negatives. Different CNNs might excel in distinct aspects of melanoma detection due to variations in their designs and training data. Decision fusion capitalizes on classifier diversity to address uncertainties and fluctuations in detection, which is crucial in real-world situations where data can be noisy or exhibit unexpected variations.