Automated skin burn detection and severity classification using YOLO Convolutional Neural Network Pretrained Model

Abstract

Skin burn classification and detection are one of topics worth discussing within the theme of machine vision, as it can either be just a minor medical problem or a life-threatening emergency. By being able to determine and classify the skin burn severity, it can help paramedics give more appropriate treatment for the patient with different severity levels of skin burn. This study aims to approach this topic using a computer vision concept that uses YOLO Algorithms Convolutional Neural Network models that can classify the skin burn degree and determine the burnt area using the bounding boxes feature from these models. This paper was made based on the result of experimentation on the models using a dataset gathered from Kaggle and Roboflow, in which the burnt area on the images was labelled based on the degree of burn (i.e., first-degree, second-degree, or third-degree). This experiment shows the comparison of the performance produced from different models and fine-tuned models which used a similar approach to the YOLO algorithm being implemented on this custom dataset, with YOLOv5l model being the best performing model in the experiment, reaching 73.2%, 79.7%, and 79% before hyperparameter tuning and 75.9%, 83.1%, and 82.9% after hyperparameter tuning for the F1-Score and mAP at 0.5 and 0.5:0.95 respectively. Overall, this study shows how fine-tuning processes can improve some models and how effective these models doing this task, and whether by using this approach, the selected models can be implemented in real life situations.