## 3 Materials and Methods
### Classification Model  
In order to select a model for the classification module, we conducted an evaluation of several web-based tool models. This evaluation involved comparing them in terms of accuracy, F1 score, and their suitability for integration into an EHR system. The comparison, as documented in [37], specifically targeted Google Teachable Machine, Google Vertex AI, Salesforce Einstein Vision, and Microsoft Azure Machine Learning, utilizing exclusively melanoma database images. The experimental results are presented comprehensively in Table 2.  
Another comparison we conducted is detailed in [39], where we expanded the comparison to include more databases and pathologies. This comparison also involved GT, Google Vertex AI, Salesforce Einstein Vision, and Microsoft Azure Machine Learning, using 10 image databases (detailed in Section 3.1). These web-based tools were trained to detect multiple pathologies simultaneously. The classes and the number of images are provided in Table 1 and the results in Table 3. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Web-Based Model** & **Database** & **Accuracy** & **F1 Score** \\ \hline \multirow{2}{*}{Google Teachable Machine} & DermIS & 0.78 & 0.77 \\  & ISIC & 0.76 & 0.75 \\ \hline \multirow{2}{*}{Google Vertex AI} & DermIS & 0.83 & 0.83 \\  & ISIC & 0.74 & 0.73 \\ \hline \multirow{2}{*}{Microsoft Azure Machine Learning} & DermIS & 0.79 & 0.79 \\  & ISIC & 0.82 & 0.82 \\ \hline \multirow{2}{*}{SalesForce Einstein} & DermIS & 0.80 & 0.80 \\  & ISIC & 0.79 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for web-based models on melanoma. The comparison covered aspects such as primary purpose, user skill level, model types supported, training ease, deployment options, integration with cloud services, customization and flexibility, ease of use for beginners, advanced features, and pricing models. From both the experiments, we completed the primary objective of assessing various machine learning models and platforms like GT, Google Vertex AI, Salesforce Einstein Vision, and Microsoft Azure Machine Learning to identify the most suitable option for integration into an EHR system equipped with an automated pathology detection module. This evaluation is crucial for informed decision making during system development, focusing on accuracy, dependability, and real-world applicability in clinical settings. Decision fusion, leveraging diverse classifiers to reduce false positives and negatives, addresses uncertainties in diverse pathology detection. Evaluating speed, resource efficiency, scalability, and cost-effectiveness further guides the selection process. GT stands out as a versatile and cost-effective option, offering seamless integration and adjustable training parameters. For creating the classification model, we used a web-based tool, GT, which functions as an online tool for creating machine learning models by utilizing TensorFlow through the TensorFlow-is library, an open-source tool for machine learning and artificial intelligence. These models are specifically designed for training and operation directly within a web browser, employing knowledge transfer learning techniques. The platform utilizes a technique called transfer learning, where a pre-trained model is fine-tuned using the user's data. This allows users to create custom machine learning models for various tasks, such as image classification, sound recognition, or pose detection, without the need to build models from scratch.