Burn Wound Severity Detection using Segmentation Model: A Comparative Study Between U-NET and YOLOv8
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Abstract
Nowadays, burn injuries are ranked as the fourth most prevalent cause of injuries on a global scale. A variety of substances, including gushing oil, scalding hot water, and chemical contact, can cause these injuries. Burn injuries have the potential to affect people of all ages and genders, but they are especially hazardous in domestic kitchens. There are three different levels of burn severity: (i) first-degree burns, (ii) second-degree burns, and (iii) third-degree burns, each of which has a unique manifestation. The severity of burn injuries dictates the treatment approach, a determination that has conventionally been entrusted to the knowledge and proficiency of medical practitioners. However, divergent approaches among practitioners in evaluating the severity of burns may result in possible inconsistencies in the interpretation of findings. In order to address this obstacle, the implementation of artificial intelligence (AI) technology may be utilized to construct a burn severity detection model. The utilization of AI in this particular context offers several benefits: (i) it facilitates the preliminary determination of burn severity, thereby alleviating the burden of healthcare practitioners; and (ii) it improves the accuracy of burn severity detection, consequently streamlining the evaluation procedure.
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