Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2354
- Type
- D - Journal article
- DOI
-
10.1109/JBHI.2018.2868656
- Title of journal
- IEEE Journal of Biomedical and Health Informatics
- Article number
- -
- First page
- 1730
- Volume
- 23
- Issue
- 4
- ISSN
- 2168-2194
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- URL
-
https://e-space.mmu.ac.uk/621458/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
B - Human Centred-Computing
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This was the first research to transform the way that diabetic foot ulcers (DFU) are detected on mobile devices using deep learning models. The outcomes formed the basis for the first DFU Challenge 2020 held at MICCAI2020, the world’s largest medical imaging conference leading to collaborations with leading professors in USA, India and New Zealand. We co-created the largest dataset of its kind (requested by 50 institutions from 25 countries) and were awarded the Oracle Innovator Accelerator Programme to scale up FootSnap-AI in partnership with Oracle Corporation (raj.modi@oracle.com). The research was reported extensively in the media (Forbes, Diabetes Times).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
