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

Titelangaben

Neuwieser, Hannah ; Jami, Naga Venkata Sai Jitin ; Meier, Robert Johannes ; Liebsch, Gregor ; Felthaus, Oliver ; Klein, Silvan ; Schreml, Stephan ; Berneburg, Mark ; Prantl, Lukas ; Leutheuser, Heike ; Kempa, Sally:
Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification.
In: Diagnostics. Bd. 15 (2025) Heft 17 . - 2184.
ISSN 2075-4418
DOI: https://doi.org/10.3390/diagnostics15172184

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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.