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

arXiv: https://arxiv.org/abs/2105.07763 · HTML: https://mlsanity.com/paper/2105.07763/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 3 of 3)

- [Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation](https://mlsanity.com/paper/1708.01928/) (2017)
- [DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/1711.10448/) (2017)
- [DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection](https://mlsanity.com/paper/2004.11853/) (2020)

## Built on by (most cited first, 7 of 7)

- [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022)
- [Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary](https://mlsanity.com/paper/2111.10376/) (2021)
- [Development of Diabetic Foot Ulcer Datasets: An Overview](https://mlsanity.com/paper/2201.00163/) (2022)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2304.12987/) (2023)
- [Diabetic Foot Ulcer Grand Challenge 2022 Summary](https://mlsanity.com/paper/2304.12001/) (2023)
- [Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation](https://mlsanity.com/paper/2503.05214/) (2025)

## Abstract (arXiv)

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 which 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.