## 5 Discussion  
In this paper, we introduced a multi-network system that employs decision fusion for melanoma detection, utilizing eight high-performance CNNs: DarkNet-53, DenseNet-201, GoogLeNet, Inception-V3, InceptionResNet-V2, ResNet-50, ResNet-101, and Xception. The selection of these CNNs was guided by a thorough analysis of the state-of-the-art. The fusion system's weights were determined based on the F1 scores achieved by each individual CNN. We evaluated the performance of each architecture on two distinct databases, considering both databases when assigning the fusion weights to enhance adaptability. As mentioned in our earlier work [6], the DullRazor algorithm could not completely remove hair from all ISIC 2020 images, partially due to the presence of annotations and thick hair in some images. In Equations (5) and (6), the variables represented by terms such as \"\(\text{d}_{\text{DarkNet}}\)\" and \"\(\text{d}_{\text{Xception}}\)\" are decisions made by the neural network architectures. The formula essentially represents a weighted combination of these measurements. Each architecture's measurement is multiplied by a specific weight (such as 0.8543 for DarkNet and 0.7513 for Xception) and then added together. This combination is represented by \"\(D_{M}\)\" and \"\(D_{NM}\)\" for two different scenarios. \[D_{M}=\text{d}_{\text{DarkNet}}\text{-}0.8543+\ldots+\text{d}_{\text{Xception}} \text{-}0.7513 \tag{5}\]  
\[D_{NM}=\text{d}_{\text{DarkNet}}\text{-}0.8599+\ldots+\text{d}_{\text{Xception}} \text{-}0.7494 \tag{6}\]  
It is notable that results obtained from the DermIS database tend to be superior to those from the ISIC 2020 database, both in the decision fusion classifier and the web-based applications. This could be attributed to the fact that ISIC 2020 comprises images collected from various sources with different equipment, conditions, and populations. To improve results, a pre-processing step involving hair removal and resizing was necessary for ISIC 2020 images. All results obtained are presented in Table 7, alongside recent work by other researchers who evaluated their models based on accuracy and F1 score. Our proposed classifier demonstrates superior performance compared to most evaluated classifiers. Although the web-based applications yielded less accurate results, they hold promise in terms of rapid prototyping without the need for pre-processing steps. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifications Methods** & **Database** & **Accuracy** & **F1 Score** \\ \hline \multirow{2}{*}{**Google Teachable Machine**} & DermIS & 0.7800 & 0.7786 \\  & ISIC & 0.7600 & 0.7597 \\ \hline \multirow{2}{*}{**Google Vertex AI**} & DermIS & 0.8333 & 0.8331 \\  & ISIC & 0.7400 & 0.7370 \\ \hline \multirow{2}{*}{**Microsoft Azure Machine Learning**} & DermIS & 0.7967 & 0.7966 \\  & ISIC & 0.8200 & 0.8200 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results for the decision fusion classifier vs. other classifiers vs. the automated applications. Both MATLAB-made classifiers proposed in this paper and web-based tools have their own advantages and use cases. MATLAB offers extensive capabilities, flexibility, and advanced image processing functionalities suitable for complex medical image analysis. On the other hand, web-based tools provide user-friendly interfaces, rapid prototyping, accessibility, collaboration features, and integration with cloud infrastructure. The choice between the two depends on the specific requirements, expertise, and preferences of the user or development team. Regarding the easiness of integration vs. performance, web-based tools are often designed with simplicity and ease of use in mind. They typically provide user-friendly interfaces and streamlined workflows that make it easy to train and deploy machine learning models. Integration into another platform can be straightforward, as these tools often offer export options or APIs to facilitate model integration.