## 3 Materials and Methods
### Classification Model
This approach streamlines model development and augments performance, especially in scenarios where labeled medical image datasets are limited. While specifics may vary across versions and implementations, here, a general overview is that the input layer receives the input data, usually images or visual data. Convolutional layers, comprising multiple filters, extract features by scanning for patterns at different spatial scales. Activation functions introduce non-linearity for learning complex data relationships. Pooling layers downsample feature maps while retaining crucial information. Fully connected layers transform extracted features for prediction, often with numerous parameters to learn intricate patterns. The output layer generates final predictions or classifications, such as image categorization in GT. Regarding the training process, GT comprises several stages. Firstly, users gather and prepare their dataset, which contains examples of the classes or categories the model will recognize. Next, the dataset is uploaded onto the GT platform, which offers the option to upload various types of data, such as images, sounds, or poses, depending on the model's requirements. Following that, each example in the dataset is labeled with its corresponding class or category. Then, the GT platform utilizes transfer learning techniques to train a pre-existing machine learning model with the provided dataset. This involves fine-tuning a pre-trained model with the user's data to tailor it to the specific task at hand. After the training is complete, users can validate the model to evaluate its performance. This typically entails testing the model on a separate validation dataset to assess its accuracy and performance metrics. Finally, upon validation, the trained model can be deployed for use in various applications. Users have the option to export the model for integration into their own projects, such as websites, apps, or other software applications. Regarding the real-world application of GT for detecting pathologies depends on several key factors. Accessibility is paramount; GToffers an intuitive interface, making it accessible to a wide audience, including healthcare professionals, researchers, educators, and students, regardless of their level of expertise in machine learning. Customization plays a crucial role; users can train GT models with their datasets, allowing for tailored solutions to address specific healthcare challenges and needs. Versatility is another aspect; GT supports various data inputs such as images, sounds, and poses, enabling pathology detection across different modalities like medical imaging, audio analysis, or movement recognition. Educational use is common; GT serves as a valuable tool for educational purposes, providing students and healthcare professionals with hands-on experience in pathology detection and machine learning concepts, fostering familiarity with these techniques in healthcare applications. Rapid prototyping capabilities are essential; GT facilitates quick iteration and refinement of pathology detection models, enabling experimentation with different approaches and adjustments based on feedback and performance. Integration potential is a key feature; trained models can be exported from GT and seamlessly integrated into various applications, including websites, apps, or other software systems, allowing for the deployment of pathology detection solutions in real-world healthcare settings. One of the advantages of GT is its user-friendly interface, allowing individuals to create and train machine learning models without requiring programming skills, ensuring accessibility for a diverse user base. It facilitates rapid prototyping, enabling experimentation with different ideas, crucial for testing concepts in research projects. Supporting various inputs such as images, audio, and posture, GT allows the creation of models catering to diverse needs. The use of transfer learning expedites the training process, facilitating efficient model creation with relatively small amounts of data.