Burn Wound Severity Detection using Segmentation Model: A Comparative Study Between U-NET and YOLOv8 - Dittakan, Kwankamon; Nookong, Sorawit (Journal of Applied Informatics and Technology, 2024)
This research focuses on detecting burn severity using an area of interest detection approach. The burn areas are categorized into three levels: (i) first-degree burns, (ii) second-degree burns, and (iii) third-degree burns, each with distinct visual characteristics. To detect burn severity, artificial intelligence (AI) techniques were applied through the development of two models: U-Net and YOLOv8. A dataset of 2,000 images representing all three levels of burns was used in the experiments. The goal was to compare the performance of U-Net, a widely used model for segmenting areas of interest, with YOLOv8, a newer model introduced in 2023. Experimental results show that the best performance of U-Net achieved an accuracy of 79.1% and a loss value of 79.8%. In contrast, YOLOv8 achieved its best result with an mAP@0.5 of 0.657, a segmentation loss of 3.231, and a classification loss of 1.961. These results represent the optimal performance of both models in this study.