Abstract: The research focuses on diabetic foot ulcers (DFUs), a critical complication of diabetes, and proposes an innovative approach using deep learning techniques for detection. The system maps the localized ulcers onto a foot sole blueprint, enabling the creation of custom shoes for preventive measures. By integrating data collection, model inference, and a user-friendly web interface, the system aims to revolutionize DFU management, potentially reducing severe effects and enhancing patient care. The methodology involves a comprehensive dataset, training of the YOLOv8 model, and a user interface for personalized ulcer detection. The research aims to improve patient outcomes and alleviate healthcare system burdens by enhancing DFU management through advanced technology and personalized care.
Keywords: Diabetic foot ulcer, YOLOv8, Machine learning, Detection
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DOI:
10.17148/IJARCCE.2024.134139
