Wound.Vision Automated Burn Detection and Classification System using ESP32-CAM and Computer Vision

This paper presents an open-source, low-cost system for automated detection and classification of burn wounds using an ESP32-CAM microcontroller and machine learning algorithms implemented with Edge Impulse. The system is designed to assist healthcare professionals in rapid assessment. Traditional burn assessment relies primarily on visual inspection and clinical experience, which can be subjective and time-consuming. Our prototype offers an objective, portable, and cost-effective solution that can classify burn grades automatically through a trained model deployed using Edge Impulse and implemented in Arduino IDE. The system integrates hardware components including ESP32-CAM module with WiFi connectivity to send results directly to healthcare professionals' mobile devices via the Blynk IoT platform. Initial validation demonstrates the system's capability to distinguish between different burn severities with potential applications in telemedicine, emergency response, and resource-limited healthcare settings.