## 3 Materials and Methods
### Classification Model
Models created in GT can be exported as TensorFlow.js models, seamlessly incorporated into web applications without the need for a complex infrastructure. However, when considering integration with another platform, certain drawbacks should be taken into account. While GT is designed for accessibility, it may have limitations in customization compared to more advanced machine learning frameworks. Its simplified workflows, while advantageous for rapid prototyping, may lack the same level of control as more complex frameworks, necessitating extensive development outside GT for advanced customization or model optimization. Integrating GT into another platform may create a dependency on Google's infrastructure and ecosystem, potentially limiting flexibility for future migrations or changes. Additionally, GT is tailored for simple, small-scale machine learning projects and may not be suitable for larger applications with high computational requirements. While Google provides documentation and resources for Teachable Machine, the level of support and resources may be limited compared to more widely used machine learning frameworks. As GT primarily operates as a web-based tool, offline implementation or integration with non-web-based platforms may require additional development effort to adapt the GT model. Despite these considerations, in the context of the project's objective, which involves integrating an image classification model, GT remains versatile, free, and easily integrable into platforms using APEX as the programming language. In our pursuit of developing a robust and adaptable classification model for accurately detecting eye and skin pathologies, we adopted a meticulous approach to curate our training datasets. Recognizing the importance of ensuring that our training data accurately represents real-life scenarios, we implemented several techniques to achieve this goal. First and foremost, we prioritized the utilization of diverse and representative training data. By assembling a dataset that captures the variability and complexity of real-world situations, we provided our model with the opportunity to learn robust patterns and relationships that can be effectively generalized to unseen data. This foundational approach lays the groundwork for the model's ability to navigate the intricacies of diagnosing eye and skin conditions with precision and accuracy. To further bolster the adaptability of our classification model, we meticulously sourced data from various databases, strategically targeting images afflicted by a myriad of challenges. This included images affected by noise, poor resolution, varying lighting conditions,equipment disparities, and the presence of artifacts. For example, within our skin conditions dataset, we ensured inclusion of images boasting diverse textures, colors, and body locations. Similarly, our eye pathologies dataset encompassed fundus images spanning individuals of varying ages, severity levels, ethnicities, and other pertinent factors. This deliberate selection process enabled our model to familiarize itself with the diverse array of real-world scenarios it may encounter in clinical practice. In addition, we took proactive measures to balance the distribution of data across different classes or categories within our datasets. By mitigating biases and promoting effective generalization of the model, we sought to prevent skewed predictions and to mitigate the risk of inferior performance on underrepresented classes. This meticulous attention to dataset balance enhances the reliability and robustness of our classification model's predictions. Lastly, to validate the efficacy of our trained model, we subjected it to rigorous evaluation using separate validation datasets. These validation datasets were thoughtfully crafted to closely resemble the distribution of real-world data encountered by the system in practical settings.