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.本文提出一款开源低成本的烧伤创面自动检测与分类系统，该系统采用ESP32-CAM微控制器，并借助Edge Impulse部署机器学习算法，旨在协助医护人员开展快速伤情评估。传统烧伤评估主要依赖目视检查与临床经验，存在主观性强且耗时较长的弊端。本原型系统提供了一种客观、便携且成本可控的解决方案，可通过基于Edge Impulse部署、并在Arduino IDE中实现的训练模型自动完成烧伤等级分类。本系统集成了搭载WiFi功能的ESP32-CAM模块在内的硬件组件，可通过Blynk物联网平台将检测结果直接推送至医护人员的移动设备。初步验证结果表明，本系统能够区分不同严重程度的烧伤，有望应用于远程医疗、应急救援以及医疗资源匮乏的场景中。