AUTOMATIC WOUND DETECTION AND MEASUREMENT SYSTEM IMPLEMENTED IN YOLOV7, FOR FUTURE WOUND HEALING APPLICATION
DOI:
https://doi.org/10.5377/nexo.v38i01.20588Keywords:
Wound-detection, YOLOv7, scrapes, cuts-and-bruisesAbstract
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
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