## 4 Experimental Results
### Experimental Results for Skin Lesion Detection Using the Automated Applications
It is vital to test the models on a diverse dataset encompassing various types of melanoma images to gauge their resilience and ability to apply to different scenarios. Robust models can handle changes in lighting, perspectives, and skin tones. The comparative analysis seeks to assess how clinically applicable each solution is within the healthcare context, as precise and reliable outcomes are crucial for informed clinical decisions. Evaluating the speed of inference and resource efficiency for each model holds significance, particularly in a real-time healthcare environment. Models that deliver accurate results swiftly are preferred for integration into an EHR system. Real-time performance is essential for seamless incorporation into the fast-paced realm of healthcare delivery. The evaluation also involves scrutinizing the integration potential of each solution into the existing EHR system infrastructure. Factors such as compatibility, API, and deployment choices must be taken into account. A solution that seamlessly aligns with the system's architecture saves time and resources during implementation. Furthermore, scalability is assessed for each solution, gauging their ability to accommodate larger datasets and future expansion. Solutions that can scale in accordance with the evolving needs of a growing healthcare practice are sought after. The capacity of the chosen solution to scale alongside increased data and usage is imperative for long-term viability. Cost analysis is crucial to ascertain the expenses associated with adopting each solution and to ensure compatibility with the project's budget. The cost-effectiveness of each solution is evaluated, encompassing development, deployment, and maintenance expenses. Optimal value delivery within the project's financial constraints is a pivotal consideration. Finally, the user interface and user experience of the chosen solution must align with the design and user requisites of the EHR system. By systematically evaluating these aspects, we can determine which of the evaluating models or platforms is the most reliable and effective solution to integrate into the EHR system with an automated melanoma detection module. This analysis will help inform the decision-making process during the planning and solution analysis phases of the EHR system development project. In the present paper, we assessed the way in which we would approach the melanoma detection module by developing a custom decision fusion melanoma classifier and comparing it with some of the well-known web-based models online. After evaluating the results, Google Teachable Machine emerges as a highly versatile option. The model exhibits commendable accuracy and offers seamless integration into architectures utilizing Java as the programming language. The process of uploading images, training the model, and subsequent cloud-based utilization is provided without any charge. An additional advantage lies in the flexibility to adjust network training parameters, such as learning rate, batch size, and number of epochs, for optimal outcomes. This renders the application suitable for integration into an EHR system. For a comprehensive overview, we present a comparison of accuracy and F1 scores among the four web-based applications in Table 5.