Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation

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

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 summarises 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 enhanced the F1-Score but not the mAP.