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Authors: Artur Leandro da Costa Oliveira ; André Britto de Carvalho and Daniel Oliveira Dantas

Affiliation: Departamento de Computação, Universidade Federal de Sergipe, São Cristóvão, SE, Brazil

Keyword(s): Deep Learning, Convolutional Neural Networks.

Abstract: Diabetic Foot Ulcer (DFU) is one of the major health concerns about Diabetes. These injuries impair the patient’s quality of life, bring high costs to public health, and can even lead to limb amputations. The use of automatic tools for detection can assists specialists in the prevention and treatment of the disease. Some methods to address this problem based on machine learning have recently been presented. This article proposes the use of deep learning techniques to assist the treatment of DFUs, more specifically, the detection of ulcers through photos taken from the patient’s feet. We propose an improvement of the original Faster R-CNN using data augmentation techniques and changes in parameter settings. We used a training dataset with 2000 images of DFUs annotated by specialists. The training was validated using the Monte Carlo cross-validation technique. Our proposal achieved a mean average precision of 91.4%, a F1-score of 94.8%, and an average detection speed of 332ms which out performed traditional detector implementations. (More)

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Paper citation in several formats:
Oliveira, A.; Britto de Carvalho, A. and Dantas, D. (2021). Faster R-CNN Approach for Diabetic Foot Ulcer Detection. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 677-684. DOI: 10.5220/0010255506770684

@conference{visapp21,
author={Artur Leandro da Costa Oliveira. and André {Britto de Carvalho}. and Daniel Oliveira Dantas.},
title={Faster R-CNN Approach for Diabetic Foot Ulcer Detection},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={677-684},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010255506770684},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Faster R-CNN Approach for Diabetic Foot Ulcer Detection
SN - 978-989-758-488-6
IS - 2184-4321
AU - Oliveira, A.
AU - Britto de Carvalho, A.
AU - Dantas, D.
PY - 2021
SP - 677
EP - 684
DO - 10.5220/0010255506770684
PB - SciTePress

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