A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection

Manu Goyal; Saeed Hassanpour

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arXiv · 2007.07922

A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection

Manu Goyal·Saeed Hassanpour

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Abstract

Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes. Each year, more than 1 million diabetic patients undergo amputation due to failure to recognize DFU and get the proper treatment from clinicians. There is an urgent need to use a CAD system for the detection of DFU. In this paper, we propose using deep learning methods (EfficientDet Architectures) for the detection of DFU in the DFUC2020 challenge dataset, which consists of 4,500 DFU images. We further refined the EfficientDet architecture to avoid false negative and false positive predictions. The code for this method is available at https://github.com/Manugoyal12345/Yet-Another-EfficientDet-Pytorch.

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Manu Goyal, Saeed Hassanpour. 2020-07-15. A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection. https://arxiv.org/abs/2007.07922

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