Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

317
Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...
317
Peripheral Artery Disease IV: Nursing Management01:26

Peripheral Artery Disease IV: Nursing Management

314
 The nursing management of a patient with peripheral artery disease (PAD) begins with a thorough assessment of the patient’s health history and clinical manifestations.AssessmentHealth History: Evaluate the patient’s history of hypertension, hyperlipidemia, family history of cardiovascular issues, and lifestyle factors such as dietary patterns, smoking, and physical activity.Physical Examination:Assess the affected extremity for decreased or absent peripheral pulses,...
314
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

290
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
290
Traditional Level Of Health Care System01:26

Traditional Level Of Health Care System

3.3K
The levels of care describe the services provided in the healthcare system. Accordingly, there are six levels of the traditional healthcare system in the US: preventive, primary, secondary, tertiary, restorative, and continuing healthcare. A nurse must understand how the healthcare industry organizes and provides services within these levels of care.
The preventive healthcare service includes tests for screening. Preventive health care services include identifying and reducing disease risk...
3.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Attention-enhanced deep temporal fusion network for cloud-IIoT prognostics of EV induction motors under dynamic loads.

Scientific reports·2026
Same author

Parkinson disease severity detection based On OPtFuzNet with fused features.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2026
Same author

Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.

Frontiers in artificial intelligence·2026
Same author

Multi-scale adaptive fusion network for retinal layer and fluid segmentation in optical coherence tomography B-scans.

Scientific reports·2026
Same author

Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network.

Frontiers in artificial intelligence·2026
Same author

HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification.

Frontiers in artificial intelligence·2026

Related Experiment Video

Updated: Jan 7, 2026

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model
08:16

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

Published on: March 16, 2022

3.9K

Diabetic foot ulcer classification using an enhanced coordinate attention integrated ConvNext model.

L Jani Anbarasi1, R Neeraja2, S Geetha1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Physical and Engineering Sciences in Medicine
|January 5, 2026
PubMed
Summary

This study presents an AI-driven method for diagnosing diabetic foot ulcers (DFUs) from images, improving accuracy and efficiency. The automated approach aids in early detection and management, potentially reducing amputation risks.

Keywords:
Adaptive thresholdingConvNeXt modelCoordinate attention modelDiabetic foot ulcer

More Related Videos

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers
04:09

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers

Published on: March 3, 2023

3.6K
A Simplified Technique for Producing an Ischemic Wound Model
12:00

A Simplified Technique for Producing an Ischemic Wound Model

Published on: May 2, 2012

17.7K

Related Experiment Videos

Last Updated: Jan 7, 2026

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model
08:16

High-Resolution Three-Dimensional Imaging of the Footpad Vasculature in a Murine Hindlimb Gangrene Model

Published on: March 16, 2022

3.9K
Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers
04:09

Prospective, Randomized, and Controlled Study of a Human Umbilical Cord Mesenchymal Stem Cell Injection for Treating Diabetic Foot Ulcers

Published on: March 3, 2023

3.6K
A Simplified Technique for Producing an Ischemic Wound Model
12:00

A Simplified Technique for Producing an Ischemic Wound Model

Published on: May 2, 2012

17.7K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Diabetes Management

Background:

  • Diabetic foot ulcers (DFUs) are a serious diabetes complication, often leading to amputation.
  • Current DFU management is costly and requires intensive monitoring, with limitations in pattern recognition for accurate classification.
  • Existing methods struggle with identifying complex patterns and contextual correlations in DFU images.

Purpose of the Study:

  • To develop an automated, deep learning-based approach for enhanced Diabetic Foot Ulcer (DFU) assessment using medical images.
  • To improve the accuracy and efficiency of DFU investigation and recommendation processes.
  • To overcome limitations in current DFU diagnostic methods regarding pattern recognition and contextual analysis.

Main Methods:

  • Employed adaptive thresholding to enhance DFU image contrast and uniformity for improved feature extraction.
  • Utilized a hybrid deep learning model combining ConvNeXt architecture with coordinate attention for DFU image classification.
  • Incorporated coordinate attention to capture spatial information, enhancing the extraction of long-range dependency features.

Main Results:

  • The developed model achieved a high classification accuracy of 97.16%.
  • An F1-score of 0.97 was obtained, indicating robust performance in DFU identification.
  • The attention-enhanced ConvNeXt model demonstrated effective representation of complex patterns in DFU images.

Conclusions:

  • The proposed attention-enhanced deep learning model offers a promising automated solution for DFU assessment.
  • This approach can expedite DFU investigation, leading to more optimal treatment recommendations.
  • The findings suggest a significant advancement in leveraging AI for improved diabetes complication management.