An attention-guided dense convolutional network for automated diabetic foot ulcer segmentation

Abstract

Introduction: A large number of diabetic patients have diabetic foot ulcers (DFU), and it is essential to segment the regions of the ulcer in a timely and accurate manner to treat them. The segmentation task is very difficult due to the highly irregular shape of the ulcers and very small areas of lesions, up to 0.04% of the DFU image.

Methods: We aim to develop an attention-guided dense convolutional network, called UlcerNet, which is able to extract fine-grained ulcer features at different scales. It introduces dense convolutions, multi-scale residual attention blocks (MRADB), and modified receptive-field modules (MRFB) to enhance feature propagation and context modeling. We use the public DFUC 2022 dataset that contains 4000 close-up foot images to train and evaluate the UlcerNet model.

Results: Our model is able to perform a Dice of 73.50% and mIoU of 76.04%, which gives better performance than the standard segmentation baselines (FCN8 49.93%, SegNet 47.68%, DeepLabV3 + 62.77%) and recent DFU segmentation baselines (TranUNet 42.61%, HarDNet-DFUS 59.81%, UFOS-Net 73.09%).

Discussion: An ablation study further shows that each component, such as dense blocks, channel attention, spatial attention, MRADB, and MRFB, is significant for the accuracy. These findings show that UlcerNet offers good and precise DFU segmentation, which can be useful for clinical settings in the real-world.

Automated corneal endothelial cell detection and segmentation in specular microscopy images using Attention and Faster U-Net
Ashwini Ramchandra Doke, Suja Palaniswamy
Scientific Reports13 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textMedical imaging & radiologyCrossref
Automated correction of training sets for biological image segmentation by convolutional networks
Jacques Boutet de Monvel, Raphaël Etournay
HAL27 Aug 2026PreprintCC BY 4.0Full textSystems & synthetic biology StatisticsHAL
Feature-aware attention convolutional neural network for efficient pulmonary fibrosis classification from chest CT images with FPGA deployment
K. Mahapackialakshmi, G. Jaffino
Frontiers in medicine16 Oct 2026Peer-reviewedCC BYFull textGeneral medicineEurope PMC
Interpretable attention-deficit hyperactivity disorder diagnosis from functional magnetic resonance imaging via hybrid convolutional neural network–transformer learning and multi-level graph-based comprehensibility
R. Harini, O. Uma Maheswari, K. Lakshmi Priya
Scientific Reports5 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textPsychiatry & mental healthCrossref
COVLIAS 3.5: integration of attention-based segmentation technique with fuzzy dilated convolutional neural networks for improved classification of chest X-ray scans for multiclass pneumonia diagnosis
Arun K. Dubey, Achin Jain, Shruti Vashist +4
Scientific Reports1 Jul 2026Peer-reviewedCC BYFull textMedical imaging & radiologyDOAJ

Cite

BibTeX RIS

BibTeX

@article{r2026attentionguided,
  title = {{An attention-guided dense convolutional network for automated diabetic foot ulcer segmentation}},
  author = {Rampriya R. S. and Sabari Nathan and Arumuga Arun R.},
  journal = {Frontiers in Endocrinology},
  year = {2026},
  month = oct,
  doi = {10.3389/fendo.2026.1940557},
  url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.1940557/full},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - An attention-guided dense convolutional network for automated diabetic foot ulcer segmentation
AU  - Rampriya R. S.
AU  - Sabari Nathan
AU  - Arumuga Arun R.
PY  - 2026
DA  - 2026/10/16
JO  - Frontiers in Endocrinology
DO  - 10.3389/fendo.2026.1940557
UR  - https://www.frontiersin.org/articles/10.3389/fendo.2026.1940557/full
AB  - Introduction: A large number of diabetic patients have diabetic foot ulcers (DFU), and it is essential to segment the regions of the ulcer in a timely and accurate manner to treat them. The segmentation task is very difficult due to the highly irregular shape of the ulcers and very small areas of lesions, up to 0.04% of the DFU image.  Methods: We aim to develop an attention-guided dense convolutional network, called UlcerNet, which is able to extract fine-grained ulcer features at different scales. It introduces dense convolutions, multi-scale residual attention blocks (MRADB), and modified receptive-field modules (MRFB) to enhance feature propagation and context modeling. We use the public DFUC 2022 dataset that contains 4000 close-up foot images to train and evaluate the UlcerNet model.  Results: Our model is able to perform a Dice of 73.50% and mIoU of 76.04%, which gives better performance than the standard segmentation baselines (FCN8 49.93%, SegNet 47.68%, DeepLabV3 + 62.77%) and recent DFU segmentation baselines (TranUNet 42.61%, HarDNet-DFUS 59.81%, UFOS-Net 73.09%).  Discussion: An ablation study further shows that each component, such as dense blocks, channel attention, spatial attention, MRADB, and MRFB, is significant for the accuracy. These findings show that UlcerNet offers good and precise DFU segmentation, which can be useful for clinical settings in the real-world.
SN  - 1664-2392
ER  -

Abstract

Related

Cite