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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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E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification.

Nouf F Almufadi1, Haifa F Alhasson1, Shuaa S Alharbi1

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Biomolecules & Biomedicine
|September 25, 2024
PubMed
Summary
This summary is machine-generated.

This study presents an automated method using transfer learning to detect diabetic foot ulcers (DFU) and classify them as ischemic or infected. A novel model, E-DFu-Net, achieved high accuracy, improving early detection to prevent amputations.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Diabetic Complications Management

Background:

  • Diabetic Foot Ulcer (DFU) affects 33% of global diabetes patients, frequently leading to limb amputation.
  • Early detection and accurate classification of DFU subtypes (ischemic vs. infection) are critical but challenging.
  • Current diagnostic methods for DFU can be subjective and time-consuming.

Purpose of the Study:

  • To develop and evaluate an automated approach for identifying and classifying Diabetic Foot Ulcers (DFU) using transfer learning.
  • To compare the performance of various pre-trained Deep Convolutional Neural Network (DCNN) models for DFU detection.
  • To introduce a novel DCNN model, E-DFu-Net, designed for improved DFU classification and reduced overfitting.

Main Methods:

  • Utilized transfer learning with seven pre-trained Deep Convolutional Neural Network (DCNN) models: EfficientNetB0, DenseNet121, ResNet101, VGG16, MobileNetV2, InceptionV3, and InceptionResNetV2.
  • Developed a novel model, E-DFu-Net, by adapting existing DCNN architectures to enhance DFU classification accuracy and prevent overfitting.
  • Evaluated model performance on DFU datasets, focusing on accuracy in distinguishing between ischemic and infected ulcer states.

Main Results:

  • The proposed E-DFu-Net model demonstrated superior performance in DFU classification.
  • Achieved 97% accuracy for classifying ischemic diabetic foot ulcers.
  • Achieved 92% accuracy for classifying infected diabetic foot ulcers.

Conclusions:

  • Automated DFU detection and classification using transfer learning, particularly with the E-DFu-Net model, significantly enhances diagnostic capabilities.
  • The developed approach offers a promising tool for early and accurate identification of DFU subtypes, potentially reducing amputation rates.
  • This advancement supports healthcare practitioners by providing a reliable method for managing diabetic foot complications.