Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

Yap, MH ORCID: https://orcid.org/0000-0001-7681-4287, Hachiuma, R, Alavi, A, Brüngel, R, Cassidy, B ORCID: https://orcid.org/0000-0003-3741-8120, Goyal, M, Zhu, H, Rückert, J, Olshansky, M, Huang, X, Saito, H, Hassanpour, S, Friedrich, CM, Ascher, DB, Song, A, Kajita, H, Gillespie, D ORCID: https://orcid.org/0000-0002-3783-9454, Reeves, ND ORCID: https://orcid.org/0000-0001-9213-4580, Pappachan, JM, O'Shea, C and Frank, E (2021) Deep learning in diabetic foot ulcers detection: A comprehensive evaluation. Computers in Biology and Medicine, 135. 104596. ISSN 0010-4825

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Abstract

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 learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.