Beyond Bandages: Customized First Aid for Different Wound Types

Automated wound detection using computer vision has emerged as a promising approach in healthcare, offering efficient and accurate identification of various acute injuries such as burns, abrasions, and traumatic wounds.Traditional manual assessment methods are time-consuming and subjective, leading to potential human errors.Leveraging state-of-the-art deep learning techniques, this study investigates the application of YOLO v8, a real-time object detection framework, for automated wound detection in diverse clinical scenarios.The research focuses on delineating the architecture and implementation details of YOLO v8, emphasizing its adaptability and efficiency in identifying and localizing acute injuries.Additionally, the paper discusses methodologies for dataset preparation, including collection, annotation, and augmentation of wound images crucial for training and validating the model, while addressing challenges such as class imbalance and ensuring model robustness across different wound appearances.