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At present, the ubiquity method to diagnose the severity of diabetic feet(DF) depends on professional podiatrists. However, in most cases, professionalpodiatrists have a heavy workload, especially in underdeveloped and developingcountries and regions, and there are often insufficient podiatrists to meet therapidly growing treatment needs of DF patients. It is necessary to develop amedical system that assists in diagnosing DF in order to reduce part of theworkload for podiatrists and to provide timely relevant information to patientswith DF. In this paper, we have developed a system that can classify and locateWagner ulcers of diabetic foot in real-time. First, we proposed a dataset of2688 diabetic feet with annotations. Then, in order to enable the system todetect diabetic foot ulcers in real time and accurately, this paper is based onthe YOLOv3 algorithm coupled with image fusion, label smoothing, and variantlearning rate mode technologies to improve the robustness and predictiveaccuracy of the original algorithm. Finally, the refinements on YOLOv3 was usedas the optimal algorithm in this paper to deploy into Android smartphone topredict the classes and localization of the diabetic foot with real-time. Theexperimental results validate that the improved YOLOv3 algorithm achieves a mAPof 91.95%, and meets the needs of real-time detection and analysis of diabeticfoot Wagner Ulcer on mobile devices, such as smart phones. This work has thepotential to lead to a paradigm shift for clinical treatment of the DF in thefuture, to provide an effective healthcare solution for DF tissue analysis andhealing status.

Deep Learning Methods for Real-time Detection and Analysis of Wagner Ulcer Classification System