## 5 Discussion
Integrating MATLAB-made classifiers into another platform may require additional steps, as MATLAB is standalone software. Depending on the platform and desired integration method, it may involve exporting the model, converting it to a compatible format, leveraging MATLAB Compiler to create standalone executables, or integrating MATLAB scripts with other programming languages. Performance comparison between MATLAB-made classifiers and web-based tools, such as Google Teachable Machine, depends on various factors, including the complexity of the problem, database size, and the specific algorithms and techniques employed. Both MATLAB and web-based tools can achieve good performance, but it ultimately depends on the implementation and customization of the classifiers. MATLAB provides extensive capabilities for customization, algorithm selection, and fine-tuning, allowing users to optimize classifiers for specific tasks. This flexibility may result in better performance for complex or domain-specific applications where advanced image processing or tailored algorithms are required. Web-based tools, such as Google Teachable Machine, are designed for ease of use and rapid prototyping. They often leverage pre-trained models or simplified workflows, which may provide good performance for simpler tasks or generic image classification. However, they may have limitations in terms of customization and adaptability to specific requirements. Ultimately, the choice between MATLAB-made classifiers and web-based tools for integration into another platform depends on the specific requirements, available expertise, and the desired trade-off between ease of integration and performance optimization. If the integration process needs to be simple and streamlined, with a focus on ease of use, web-based tools, such as Google Teachable Machine, may be a more suitable choice. On the other hand, if customization, advanced algorithms, and fine-tuning are critical for achieving optimal performance, MATLAB, with its extensive toolset and flexibility, may be the preferred option. The novelty and distinctions of this work lie in the fact that this paper focuses on enhancing the performance of a multi-network system through decision fusion for melanoma detection to leverage the strengths of diverse CNN architectures, potentially leading to improved accuracy. The selection of CNN architectures is not arbitrary but guided by \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifications Methods** & **Database** & **Accuracy** & **F1 Score** \\ \hline
**SalesForce** & DermIS & 0.8033 & 0.8025 \\
**Einstein** & ISIC & 0.7933 & 0.7900 \\ \hline
**Proposed Decision Fusion Classifier** & DermIS & 0.9550 & 0.9550 \\  & ISIC & 0.9350 & 0.9350 \\ \hline
**Proposed by**[85] & ISIC 2020 & 0.904 & 0.448 \\ \hline
**Proposed by**[86] & ISIC 2017 & 0.76 & 0.73 \\ \hline
**Proposed by**[87] & ISIC & 0.76 & 0.76 \\ \hline
**Proposed by**[88] & ISIC & 0.94 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._specific criteria outlined in the methodology section, and furthermore, instead of treating all networks equally, the paper introduces a weighted decision fusion system. The weights assigned to each network are based on the F1 scores achieved by each CNN. This weighting approach considers the networks' individual strengths in melanoma detection. This indicates a deliberate and informed approach to choosing networks that are best suited for the task at hand. On the other hand, the system's performance optimization takes into consideration two distinct databases, ISIC 2020 and DermIS, that employ real-world data consisting of 1200 images in total for training, validation, and testing. This practical evaluation underscores the system's applicability to genuine scenarios. The assignment of weights considers both databases, which contributes to the adaptability of the system to varying datasets. This dual-database approach acknowledges the need for robustness across different data sources.