Journal article

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

Moi Hoon Yap, Ryo Hachiuma, Azadeh Alavi, Raphael Brungel, Bill Cassidy, Manu Goyal, Hongtao Zhu, Johannes Ruckert, Moshe Olshansky, Xiao Huang, Hideo Saito, Saeed Hassanpour, Christoph M Friedrich, David Ascher, Anping Song, Hiroki Kajita, David Gillespie, Neil D Reeves, Joseph Pappachan, Claire O'Shea Show all



There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R-CNN, three variants of Faster R-CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learni..

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Awarded by National Health and Medical Research Council

Funding Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation for the use of GPUs during the challenge and for sponsoring our event. A.A., D.B.A. and M.O. were supported by the National Health and Medical Research Council [GNT1174405] and the Victorian Government's OIS Program. The work of R.B. was partially funded by a PhD grant from the University of Applied Sciences and Arts Dortmund, 44227 Dortmund, Germany.