Smart Derma: A Cloud-Based Deep Learning Ensemble for Skin Disease Identification

Abstract

Getting an early and accurate diagnosis of skin diseases is crucial for effective treatment and preventing serious complications. Traditional diagnostic methods often depend heavily on the expertise of specialists, which can be time-consuming and hard to access in many areas. In this research, we introduce Smart Derma, an AI-powered system for identifying skin diseases that utilizes a combination of deep learning models and cloud computing technologies. Our ensemble approach integrates MobileNetV3, trained from scratch on a diverse dermatological dataset that exceeds 6GB. The goal of our model is to achieve high classification accuracy while keeping latency low during inference. We leverage AWS services for deployment, ensuring scalable, real-time access through a lightweight frontend and backend system. Extensive experiments show that our ensemble model consistently outperforms individual networks in terms of accuracy, precision, recall, and F1-score. Moreover, the system maintains high responsiveness despite the large model size by optimizing resource allocation on cloud instances. Smart Derma not only enables quicker and more accurate diagnoses but also sets the stage for future mobile health applications that can be accessed in low-resource settings. This research underscores the potential of integrating AI with cloud computing as a promising strategy to address gaps in healthcare delivery. Future efforts will focus on expanding the dataset to include rare skin conditions, improving model generalization, and adding multilingual support for broader usability across diverse populations.