ArticleComputational intelligence and neuroscience2018
A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks.
Article in Computational intelligence and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.
- Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Convolutional Neural Networks in Chronic Wound Segmentation and Tissue Classification Using Real-World Images.International wound journal · 2026Article
- Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Development of a multi-task learning framework with gradnorm for precise wound tissue analysis.PloS one · 2026Article
- Wound Segmentation with U-Net Using a Dual Attention Mechanism and Transfer Learning.Journal of imaging informatics in medicine · 2025Article
- Diagnostic performance of artificial intelligence for dermatological conditions: a systematic review focused on low- and middle-income countries to address resource constraints and improve access to specialist care.International journal of emergency medicine · 2025Review
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
- Application of deep learning in wound size measurement using fingernail as the reference.BMC medical informatics and decision making · 2024Article
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.Journal of medical systems · 2024Review
- Automated wound segmentation and classification of seven common injuries in forensic medicine.Forensic science, medicine, and pathology · 2024Article
- Integrated image and location analysis for wound classification: a deep learning approach.Scientific reports · 2024Article
- Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds.Frontiers in medicine · 2024Article
- Development and validation of interpretable machine learning models for postoperative pneumonia prediction.Frontiers in public health · 2024Article
- 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System.Sensors (Basel, Switzerland) · 2023Article
- Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.JMIR mHealth and uHealth · 2022Article
- DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel.PeerJ. Computer science · 2022Article
- Advances in non-invasive biosensing measures to monitor wound healing progression.Frontiers in bioengineering and biotechnology · 2022Review
- Image segmentation using transfer learning and Fast R-CNN for diabetic foot wound treatments.Frontiers in public health · 2022Article
- Wound Size Imaging: Ready for Smart Assessment and Monitoring.Advances in wound care · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.
Indexed as
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What OpenQuestion holds
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.