A Deep Learning-Based Diagnostic Model for Skin Pigmentary Diseases in an IoT Smart Healthcare Environment

In today's medical field, the application of deep learning techniques is coming to the forefront. Especially in dermatologic diagnosis, the accuracy of the model is crucial for early diagnosis and treatment. In this paper, we describe in detail a deep learning-based dermatology detection system, especially for melanoma detection. First, we describe the whole process of data collection, preprocessing and model training. Special emphasis is placed on data augmentation techniques to address the problem of unbalanced datasets and to increase the robustness of the model. Through several iterations and fine-tuning, we finally realize a model with high accuracy. To increase the usefulness of the model, we deployed it in IoT devices, especially in an Edge Computing environment. This not only ensures low latency and real-time performance, but also enhances data privacy protection. The article also explores in detail the data flow and processing strategies that ensure data integrity and privacy. In addition to accuracy, model interpretability is also a central topic. We employ the Grad-CAM technique to generate heat maps, which allows us to visualize the areas that the model focuses on when making decisions. Compared to the actual images, this further enhances our confidence in the model's decision-making process. Overall, this paper provides a comprehensive, efficient and practical deep learning solution for the early diagnosis and analysis of skin diseases, especially melanoma, for the further development of modern medical technology.