## 6 Conclusions  
In conclusion, the incidence of melanoma cases is on the rise, necessitating the need for early detection to prevent further growth. Deep learning has shown promising results in melanoma detection, with increased accuracy observed over time. Our endeavor to develop an EHR system with an automated melanoma detection system has led us to meticulously assess the computational complexity of our approach, which involves the integration of a custom decision fusion classifier utilizing multiple neural networks. This endeavor necessitates a thorough justification, considering the intricate nature of the problem, the significance of accuracy, and the complexities involved in implementation and integration. Addressing the challenge of automating melanoma detection within an EHR system demands a solution that is both sophisticated and accurate. Our custom decision fusion classifier, which combines neural networks such as DarkNet-53, DenseNet-201, GoogLeNet, Inception-V3, InceptionResNet-V2, ResNet-50, and ResNet-101, emerges as a pioneering resolution. This multifaceted approach harmonizes effectively with the intricacies of melanoma detection, enabling us to exploit the strengths of diverse neural networks to enhance accuracy. Our system, built upon a fusion of high-performance CNN architectures including DarkNet-53, DenseNet-201, GoogLeNet, Inception-V3, InceptionResNet-V2, ResNet-50, ResNet-101, and Xception, demonstrates a clear superiority over existing applications in melanoma detection. The decision fusion procedure, optimized based on the F1 scores of each network, exhibits a level of accuracy, precision, and sensitivity that sets a new standard in this critical domain. The experimental evaluation of our proposed multi-network decision fusion system for melanoma detection yields significant implications for both medical research and clinical practice. The outcomes of our rigorous evaluation underscore the remarkable performance and distinct advantages of our approach. The empirical evaluation, conducted on diverse datasets, including ISIC 2020 and DermIS, reaffirms our system's adaptability across different image sources, enhancing its potential for real-world deployment. Our careful consideration of two distinct databases in weight assignment contributes to robust and versatile performance. As indicated in Table 7, our suggested decision fusion classifier displayed the most impressive performance when considering the DermIS database. It achieved a value of 0.9550 for both accuracy and F1 score. Among the other four applications we assessed, Google Vertex AI yielded the next best outcome with an accuracy of merely 0.8333 and an F1 score of 0.8331. This underscores the demonstrated superiority of our proposed system. In the case of ISIC, our system exhibited an accuracy and F1 score of 0.9350. In light of the decision fusion classifier's complexity, we conducted an extensive comparison with established applications, namely Google Teachable Machine, Microsoft Azure Machine Learning, Google Vertex AI, and Salesforce Einstein Vision. This comprehensive evaluation substantiated that our approach yields substantial accuracy gains, thereby validating its justification. While the integration complexities presented by the decision fusion classifier are acknowledged, the commendable performance it delivers serves as a counterbalance, accentuating its significance. The implications of these results are substantial. Our proposed multi-network decision fusion system not only advances the state of the art in melanoma detection but also holds immense potential for clinical implementation. By offering superior accuracy and a refined decision-making process, our system stands as a powerful tool for medical practitioners, aiding in early detection and improving patient outcomes. Given the medical context of melanoma detection, precision is of paramount importance. The combination of neural networks within our custom decision fusion classifier contributes synergistically to elevating accuracy.