AUTOMATIC WOUND DETECTION AND MEASUREMENT SYSTEM IMPLEMENTED IN YOLOV7, FOR FUTURE WOUND HEALING APPLICATION (Nexo Revista Científica, 2025)
Chronic and superficial wounds significantly impact the quality of life of patients, particularly those with diabetes and the elderly, requiring constant monitoring to prevent complications. This study proposes a deep learning method based on YOLOv7 for the automatic detection of superficial wounds (cuts, scrapes, and bruises) in images, as the first stage of a robotic system equipped with a helium plasma device to assist in healing the detected wounds. Using a dataset of 266 images of superficial wounds, the model was trained over 100 epochs, achieving an average precision of 93% (mAP@0.5). The results demonstrate promising performance in wound localization, regardless of the background, with an F1-score of 0.90. This non-invasive approach has the potential to enhance medical care by enabling fast and accurate detection, laying the groundwork for the automation of wound healing treatments