Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation

• URL: http://arxiv.org/abs/2010.03341v3
• Date: Mon, 24 May 2021 12:46:50 GMT
• Title: Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
• Authors: Moi Hoon Yap and Ryo Hachiuma and Azadeh Alavi and Raphael Brungel and Bill Cassidy and Manu Goyal and Hongtao Zhu and Johannes Ruckert and Moshe Olshansky and Xiao Huang and Hideo Saito and Saeed Hassanpour and Christoph M. Friedrich and David Ascher and Anping Song and Hiroki Kajita and David Gillespie and Neil D. Reeves and Joseph Pappachan and Claire O'Shea and Eibe Frank
• Abstract summary: This paper summarises the results of DFUC 2020 by comparing the deep learning-based algorithms proposed by the winning teams. 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.
• Score: 14.227261503586599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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