## 3 Materials and Methods
### Methodology for Skin Lesion Detection Using Web-Based Tools for Machine Learning Models  
Our future objective is to research the possibility of integrating a melanoma classification system into an EHR medical information system to optimize the diagnosis and treatment process. As mentioned earlier, we tried four web-based tools for creating machine learning models. This type of application has some advantages:  
* **Ease of Use:** Web-based tools are designed to be user-friendly, making them accessible to individuals without extensive programming or technical expertise. Google Teachable Machine, for example, provides a user-friendly interface that allows users to create and train machine learning models using their own data without writing complex code;
* **Accessibility:** Web-based tools are accessible from any device with an internet connection, making them widely available to users worldwide. This accessibility enables researchers, developers, and enthusiasts to access and utilize the tools without the need for specialized hardware or software installations;
* **Rapid Prototyping:** Web-based tools often provide a streamlined process for quickly prototyping and testing machine learning models. These tools offer pre-trained models, pre-processing capabilities, and simplified workflows that facilitate the development and iteration of models for various applications;
* **Visualization and Interactivity:** Web-based tools typically offer visualizations and interactive features that enhance the understanding and exploration of machine learning models. These tools often provide visual feedback, real-time predictions, and  
Figure 15: Inception-ResNet-V2. interactive controls, enabling users to interact with their models and gain insights into their behavior;
* **Collaboration and Sharing:** Web-based tools foster collaboration and knowledge sharing among users. They often provide functionalities for sharing models, databases, and project files, allowing researchers and developers to collaborate on projects, exchange ideas, and learn from each other's work;
* **Cloud Infrastructure:** Many web-based tools leverage cloud computing infrastructure, enabling users to offload the computational requirements of training and inference to powerful servers. This eliminates the need for local high-performance hardware and allows users to leverage scalable cloud resources for faster and more efficient model development;
* **Community Support:** Web-based tools often have active communities of users and developers who share resources, provide guidance, and offer support. This community aspect fosters learning, collaboration, and the exchange of best practices;
* **Integration with Other Tools and Services:** Web-based tools, such as Google Teachable Machine, often integrate with other services and platforms, allowing users to incorporate their models into broader applications or workflows seamlessly. These integrations may include exporting models for deployment, connecting to APIs, or integrating with popular frameworks and libraries. These advantages make web-based tools valuable for individuals and organizations seeking to explore and utilize machine learning without extensive technical barriers. They enable rapid prototyping, collaboration, and accessibility, opening doors for innovation and experimentation in various domains. On the other hand, software-wise, because we used MATLAB to create our classifier, this environment offers several advantages as well:  
* **Comprehensive Toolset:** MATLAB provides a comprehensive set of tools, functions, and libraries specifically designed for image processing and analysis. This includes built-in functions for image pre-processing, feature extraction, and machine learning algorithms, making it convenient for developing classifiers for medical images;
* **Robust Image Processing Capabilities:** MATLAB's image processing toolbox offers a wide range of functions for handling medical images, such as noise removal, image enhancement, segmentation, and feature extraction.