ArticleScientific reports2022
Multi-modal wound classification using wound image and location by deep neural network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- DRDarkNet: a hybrid deep feature engineering model for accurate autopsy image classification.International journal of legal medicine · 2026Article
- Integrating Wound Images and Clinical Text for Pressure Injury Assessment and Treatment Recommendation.Bioengineering (Basel, Switzerland) · 2026Article
- Photothermally Antibacterial Piezoelectric Composite Dressing Synergized with Endogenous Electrical Stimulation for Wound Healing.Pharmaceutics · 2026Article
- Comparing YOLO and U-net deep learning algorithms in chronic wound image segmentation.BMC medical imaging · 2026Article
- Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- A severity-aware multi-task CNN-transformer framework for explainable diabetic foot ulcer triage and mobile deployment.Frontiers in artificial intelligence · 2026Article
- Microfluidics, 3D bioprinted scaffold and organ-on-chip with biosensing activated therapeutic strategies for diabetic wound healing.Frontiers in bioengineering and biotechnology · 2026Review
- Review
- Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum.BMJ health & care informatics · 2025Article
- Interpreting Venous and Arterial Ulcer Images Through the Grad-CAM Lens: Insights and Implications in CNN-Based Wound Image Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Gaussian random fields as an abstract representation of patient metadata for multimodal medical image segmentation.Scientific reports · 2025Article
- Polymeric Nanoparticles for Wound Healing.Pharmaceutical nanotechnology · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Clinically validated classification of chronic wounds method with memristor-based cellular neural network.Scientific reports · 2024Observational
- Non-invasive techniques for wound assessment: A comprehensive review.International wound journal · 2024Review
- Research trends and hot topics of wearable sensors in wound care over past 18 years: A bibliometric analysis.Heliyon · 2024Article
- Wound management materials and technologies from bench to bedside and beyond.Nature reviews. Materials · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information were designed with the help of wound specialists. The multi-modal network was developed by concatenating the image-based and location-based classifier outputs with other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 82.48 to 100% in different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95 to 97.12% in various experiments. The proposed multi-modal network also showed a significant improvement in results from the previous works of literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.