Design and Implementation of a Real-Time CNN-Based Web Platform for Automated Skin Disease Diagnosis

Abstract: Skin diseases continue to be one of the most frequently reported health concerns worldwide, significantly impacting quality of life and incurring high healthcare costs. In rural and underserved regions, the shortage of dermatologists and diagnostic infrastructure compounds the burden. The proposed research presents an advanced, real-time, web-based diagnostic system leveraging Convolutional Neural Networks (CNNs) for automated classification of common dermatological conditions. Using a robust dataset of over 8,000 labeled skin disease images across nine disease classes, the ResNet50 CNN model was trained and optimized with modern preprocessing and augmentation techniques. The model was converted to TensorFlow Lite (TFLite) for enhanced portability and faster inference. The system is deployed via a Flask-based REST API, containerized using Docker, and exposed through a mobile-responsive frontend with webcam support and image upload features. Special emphasis is placed on accessibility, speed, privacy, and offline capabilities. Usability studies with non-technical users showed strong acceptance and fast learning curves, affirming the system's potential in telehealth and public health settings. This paper makes a strong case for AI-driven dermatological diagnostics, extending machine learning beyond theoretical models into actionable healthcare tools.