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Diagnostics
Volume 15
Issue 17
10.3390/diagnostics15172184
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification

by
Hannah Neuwieser
1,
Naga Venkata Sai Jitin Jami
2,3,
Robert Johannes Meier
4,
Gregor Liebsch
4,
Oliver Felthaus
1,
Silvan Klein
1,
Stephan Schreml
5,
Mark Berneburg
5,
Lukas Prantl
1,
Heike Leutheuser
2 and
Sally Kempa
1,*
1
Department of Plastic, Hand, and Reconstructive Surgery, University Hospital Regensburg, 93053 Regensburg, Germany
2
Ambient Assisted Living & Medical Assistance Systems, Department of Computer Science, University of Bayreuth, 95447 Bayreuth, Germany
3
Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91052 Erlangen, Germany
4
Presens, PreSens Precision Sensing GmbH, Imaging Solutions, 93053 Regensburg, Germany
5
Department of Dermatology, University Hospital Regensburg, 93053 Regensburg, Germany
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(17), 2184; https://doi.org/10.3390/diagnostics15172184
Submission received: 4 July 2025 / Revised: 1 August 2025 / Accepted: 25 August 2025 / Published: 28 August 2025
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Versions Notes

Abstract

Background/Objectives: Chronic wounds of the lower extremities, particularly arterial and venous ulcers, represent a significant and costly challenge in medical care. To assist in differential diagnosis, we aim to evaluate various advanced deep-learning models for classifying arterial and venous ulcers and visualize their decision-making processes. Methods: A retrospective dataset of 607 images (198 arterial and 409 venous ulcers) was used to train five convolutional neural networks: ResNet50, ResNeXt50, ConvNeXt, EfficientNetB2, and EfficientNetV2. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Grad-CAM was applied to visualize image regions contributing to classification decisions. Results: The models demonstrated high classification performance, with accuracy ranging from 72% (ConvNeXt) to 98% (ResNeXt50). Precision and recall values indicated strong discrimination between arterial and venous ulcers, with EfficientNetV2 achieving the highest precision. Conclusions: AI-assisted classification of venous and arterial ulcers offers a valuable method for enhancing diagnostic efficiency.
Keywords: deep learning; artificial intelligence; wound classification; arterial ulcers; venous ulcers; convolutional neural networks deep learning; artificial intelligence; wound classification; arterial ulcers; venous ulcers; convolutional neural networks

Share and Cite

MDPI and ACS Style

Neuwieser, H.; Jami, N.V.S.J.; Meier, R.J.; Liebsch, G.; Felthaus, O.; Klein, S.; Schreml, S.; Berneburg, M.; Prantl, L.; Leutheuser, H.; et al. Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification. Diagnostics 2025, 15, 2184. https://doi.org/10.3390/diagnostics15172184

AMA Style

Neuwieser H, Jami NVSJ, Meier RJ, Liebsch G, Felthaus O, Klein S, Schreml S, Berneburg M, Prantl L, Leutheuser H, et al. Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification. Diagnostics. 2025; 15(17):2184. https://doi.org/10.3390/diagnostics15172184

Chicago/Turabian Style

Neuwieser, Hannah, Naga Venkata Sai Jitin Jami, Robert Johannes Meier, Gregor Liebsch, Oliver Felthaus, Silvan Klein, Stephan Schreml, Mark Berneburg, Lukas Prantl, Heike Leutheuser, and et al. 2025. "Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification" Diagnostics 15, no. 17: 2184. https://doi.org/10.3390/diagnostics15172184

APA Style

Neuwieser, H., Jami, N. V. S. J., Meier, R. J., Liebsch, G., Felthaus, O., Klein, S., Schreml, S., Berneburg, M., Prantl, L., Leutheuser, H., & Kempa, S. (2025). Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification. Diagnostics, 15(17), 2184. https://doi.org/10.3390/diagnostics15172184

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