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Related Concept Videos

Diabetic Foot Ulcer01:31

Diabetic Foot Ulcer

Definition A diabetic foot ulcer (DFU) is a chronic, non-healing wound that develops in individuals with diabetes. It typically occurs on pressure-bearing areas such as the heel, metatarsal heads, or hallux, and carries a high risk of infection and amputation.Pathophysiology • The development of DFUs can be explained by four interconnected mechanisms: neuropathy, ischemia, infection, and impaired wound healing. • Neuropathy is the most common factor. Sensory neuropathy reduces pain perception,...
Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
Diabetic Nephropathy01:28

Diabetic Nephropathy

Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration occur due to afferent arteriolar...
Diabetic Neuropathy01:22

Diabetic Neuropathy

DefinitionDiabetic neuropathy is nerve damage caused by long-standing diabetes mellitus. It results directly from prolonged high blood sugar levels.PathophysiologyThe pathophysiology of diabetic neuropathy involves both metabolic and vascular disturbances triggered by chronic hyperglycemia.Metabolic injury: Elevated glucose levels activate the polyol pathway within nerve cells, leading to the accumulation of sorbitol and fructose. This increases oxidative stress, disrupts normal nerve...

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Related Experiment Videos

DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer

Md Tofael Ahmed Bhuiyan1, Md Abdur Rahman1, Farzan Majeed Noori2

  • 1Computational Intelligence Lab, Southeast University, Dhaka, Bangladesh.

Frontiers in Digital Health
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces DFU-GCNet, a deep learning model for classifying diabetic foot ulcers (DFUs). The model achieves high accuracy and provides interpretable results, enhancing clinical trust in automated screening.

Keywords:
DFU-GCNetdeep learningdiabetic foot ulcerexplainable AI (XAI)global context attentionmedical image classification

Related Experiment Videos

Area of Science:

  • Medical imaging
  • Artificial intelligence in healthcare
  • Diabetic wound management

Background:

  • Diabetic foot ulcers (DFUs) are a leading cause of lower extremity amputations.
  • Accurate and timely diagnosis of DFUs is critical for effective treatment.
  • Existing deep learning models for DFU detection face challenges with multi-scale lesions and lack clinical transparency.

Purpose of the Study:

  • To develop a robust and interpretable deep learning model for DFU classification.
  • To address limitations in current DFU detection methods, including scale variability and opaque decision-making.
  • To enhance clinician trust in automated diagnostic tools for diabetic foot conditions.

Main Methods:

  • Introduction of DFU-GCNet, an architecture combining inception modules and global context blocks.
  • Extraction of multi-scale features and modeling of spatial dependencies for improved pathology detection.
  • Integration of explainable AI techniques (GradCAM++, LIME, SHAP) for clinical transparency and validation using the Kaggle DFU dataset.

Main Results:

  • DFU-GCNet achieved a classification accuracy of 97.16%, with an F1-score of 0.9715 and a Matthews correlation coefficient of 0.9437.
  • The model demonstrated superior performance compared to established baselines like VGG16 and EfficientNet.
  • Explainable AI methods confirmed that the network focuses on clinically relevant wound boundaries.

Conclusions:

  • DFU-GCNet serves as a highly reliable automated screening instrument for diabetic foot ulcers.
  • The model's interpretability fosters greater clinical trust and facilitates adoption in healthcare settings.
  • This approach advances the potential of AI in managing diabetic complications and preventing amputations.