ArticleBiomolecules & biomedicine2025
E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification.
Article in Biomolecules & biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- AI-Enhanced System for Diabetic Foot Ulcer Localization and Classification Using Multi-Scale Neural CNN Model.Health science reports · 2026Article
- AI-Based Models for Diabetic Foot Ulcer Assessment: Scoping Review.JMIR diabetes · 2026Review
- An AI-driven framework for diabetic foot ulcer classification, segmentation, and depth estimation.Scientific reports · 2026Article
- Advances in Image-Based Diagnosis of Diabetic Foot Ulcers Using Deep Learning and Machine Learning: A Systematic Review.Biomedicines · 2025Review
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Authors and funding
3 authors.
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Abstract
The Diabetic Foot Ulcer (DFU) is a severe complication that affects approximately 33% of diabetes patients globally, often leading to limb amputation if not detected early. This study introduces an automated approach for identifying and classifying DFU using transfer learning. DFU is typically categorized into ischemic and infection states, which are challenging to distinguish visually. We evaluate the effectiveness of pre-trained Deep Convolutional Neural Network (DCNN) models for autonomous DFU detection. Seven models are compared: EfficientNetB0, DenseNet121, ResNet101, VGG16, MobileNetV2, InceptionV3, and InceptionResNetV2. Additionally, we propose E-DFu-Net, a novel model derived from existing architectures, designed to mitigate overfitting. Experimental results demonstrate that E-DFu-Net achieves remarkable performance, with 97% accuracy in ischemia classification and 92% in infection classification. This advancement enhances current methodologies and aids practitioners in effectively detecting DFU cases.
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