Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 23, Issue 4, Pages 1730-1741Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2868656
Keywords
Diabetic foot ulcers; deep learning; convolutional neural networks; DFU localization; real-time localization
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Funding
- NVIDIA Corporation
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Current practice for diabetic foot ulcers (DFU) screening involves detection and localization by podiatrists. Existing automated solutions either focus on segmentation or classification. In thiswork, we design deep learning methods for real-time DFU localization. To produce a robust deep learning model, we collected an extensive database of 1775 images of DFU. Two medical experts produced the ground truths of this data set by outlining the region of interest of DFU with an annotator software. Using five-fold cross-validation, overall, faster R-CNN with InceptionV2 model using two-tier transfer learning achieved a mean average precision of 91.8%, the speed of 48 ms for inferencing a single image and with a model size of 57.2 MB. To demonstrate the robustness and practicality of our solution to realtime prediction, we evaluated the performance of the models on a NVIDIA Jetson TX2 and a smartphone app. This work demonstrates the capability of deep learning in real-time localization of DFU, which can be further improved with a more extensive data set.
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