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A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers. / Cassidy, Bill; Reeves, Neil D.; Pappachan, Joseph M. et al.
In: IEEE Pervasive Computing, Vol. 21, No. 2, 01.04.2022, p. 78-86.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Cassidy, B, Reeves, ND, Pappachan, JM, Ahmad, N, Haycocks, S, Gillespie, D & Yap, MH 2022, 'A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers', IEEE Pervasive Computing, vol. 21, no. 2, pp. 78-86. https://doi.org/10.1109/MPRV.2021.3135686

APA

Cassidy, B., Reeves, N. D., Pappachan, J. M., Ahmad, N., Haycocks, S., Gillespie, D., & Yap, M. H. (2022). A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers. IEEE Pervasive Computing, 21(2), 78-86. https://doi.org/10.1109/MPRV.2021.3135686

Vancouver

Cassidy B, Reeves ND, Pappachan JM, Ahmad N, Haycocks S, Gillespie D et al. A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers. IEEE Pervasive Computing. 2022 Apr 1;21(2):78-86. Epub 2022 Jan 14. doi: 10.1109/MPRV.2021.3135686

Author

Cassidy, Bill ; Reeves, Neil D. ; Pappachan, Joseph M. et al. / A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers. In: IEEE Pervasive Computing. 2022 ; Vol. 21, No. 2. pp. 78-86.

Bibtex

@article{aa9aabd324cd4cb299d24bc2b3bf7df7,
title = "A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers",
abstract = "This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance. The system uses a cross-platform mobile framework that enables the deployment of mobile apps to multiple platforms using a single TypeScript code base. A deep convolutional neural network was deployed to a cloud-based platform where the mobile app could send photographs of patient{\textquoteright}s feet for inference to detect the presence of diabetic foot ulcers. The functionality and usability of the system were tested in two clinical settings: Salford Royal NHS Foundation Trust and Lancashire Teaching Hospitals NHS Foundation Trust. The benefits of the system, such as the potential use of the app by patients to identify and monitor their condition, are discussed.",
author = "Bill Cassidy and Reeves, {Neil D.} and Pappachan, {Joseph M.} and Naseer Ahmad and Samantha Haycocks and David Gillespie and Yap, {Moi Hoon}",
year = "2022",
month = apr,
day = "1",
doi = "10.1109/MPRV.2021.3135686",
language = "English",
volume = "21",
pages = "78--86",
journal = "IEEE Pervasive Computing",
issn = "1536-1268",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - A Cloud-Based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers

AU - Cassidy, Bill

AU - Reeves, Neil D.

AU - Pappachan, Joseph M.

AU - Ahmad, Naseer

AU - Haycocks, Samantha

AU - Gillespie, David

AU - Yap, Moi Hoon

PY - 2022/4/1

Y1 - 2022/4/1

N2 - This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance. The system uses a cross-platform mobile framework that enables the deployment of mobile apps to multiple platforms using a single TypeScript code base. A deep convolutional neural network was deployed to a cloud-based platform where the mobile app could send photographs of patient’s feet for inference to detect the presence of diabetic foot ulcers. The functionality and usability of the system were tested in two clinical settings: Salford Royal NHS Foundation Trust and Lancashire Teaching Hospitals NHS Foundation Trust. The benefits of the system, such as the potential use of the app by patients to identify and monitor their condition, are discussed.

AB - This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance. The system uses a cross-platform mobile framework that enables the deployment of mobile apps to multiple platforms using a single TypeScript code base. A deep convolutional neural network was deployed to a cloud-based platform where the mobile app could send photographs of patient’s feet for inference to detect the presence of diabetic foot ulcers. The functionality and usability of the system were tested in two clinical settings: Salford Royal NHS Foundation Trust and Lancashire Teaching Hospitals NHS Foundation Trust. The benefits of the system, such as the potential use of the app by patients to identify and monitor their condition, are discussed.

U2 - 10.1109/MPRV.2021.3135686

DO - 10.1109/MPRV.2021.3135686

M3 - Journal article

VL - 21

SP - 78

EP - 86

JO - IEEE Pervasive Computing

JF - IEEE Pervasive Computing

SN - 1536-1268

IS - 2

ER -