Currently, the screening of Wagner grades of diabetic feet (DF) still relies
on professional podiatrists. However, in less-developed countries, podiatrists
are scarce, which led to the majority of undiagnosed patients. In this study,
we proposed the real-time detection and location method for Wagner grades of DF
based on refinements on YOLOv3. We collected 2,688 data samples and implemented
several methods, such as a visual coherent image mixup, label smoothing, and
training scheduler revamping, based on the ablation study. The experimental
results suggested that the refinements on YOLOv3 achieved an accuracy of 91.95%
and the inference speed of a single picture reaches 31ms with the NVIDIA Tesla
V100. To test the performance of the model on a smartphone, we deployed the
refinements on YOLOv3 models on an Android 9 system smartphone. This work has
the potential to lead to a paradigm shift for clinical treatment of the DF in
the future, to provide an effective healthcare solution for DF tissue analysis
and healing status.Comment: 11 pages with 11 figure