TY - JOUR

T1 - The DFUC 2020 dataset

T2 - Analysis towards diabetic foot ulcer detection

AU - Cassidy, Bill

AU - Reeves, Neil

AU - Pappachan, J M

AU - Gillespie, David

AU - O'Shea, C

AU - Rajbhandari, S

AU - Maiya, A G

AU - Frank, E

AU - Boulton, A J M

AU - Armstrong, D G

AU - Najafi, B

AU - Wu, J

AU - Kochhar, R S

AU - Yap, Moi Hoon

PY - 2021/4/28

Y1 - 2021/4/28

N2 - Every 20 seconds a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a globalsolution. The International Conference on Medical Image Computing and Computer Assisted Intervention challenge, which concerns theautomated detection of diabetic foot ulcers (DFUs) using machine learning techniques, will accelerate the development of innovativehealthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems,recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app thatpatients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance ofa DFU. Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHSFoundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods ofDFU detection. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluationresults. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research

AB - Every 20 seconds a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a globalsolution. The International Conference on Medical Image Computing and Computer Assisted Intervention challenge, which concerns theautomated detection of diabetic foot ulcers (DFUs) using machine learning techniques, will accelerate the development of innovativehealthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems,recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app thatpatients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance ofa DFU. Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHSFoundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods ofDFU detection. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluationresults. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research

U2 - 10.17925/EE.2021.1.1.5

DO - 10.17925/EE.2021.1.1.5

M3 - Journal article

VL - 17

SP - 5

EP - 11

JO - European Endocrinology

JF - European Endocrinology

SN - 1758-3772

IS - 1

ER -