Pediatric BurnNet: Robust multi-class segmentation and severity recognition under real-world imaging conditions

Dfusion provides accurate, end-to-end segmentation and depth grading of pediatric burn wounds captured in uncontrolled environments. Its robust performance and modest computational demand support deployment on mobile devices, offering rapid, objective assistance for clinicians in resource-limited settings and enabling more precise triage and treatment planning for pediatric burn care.

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Publication Details

Journal
SAGE Open Medicine
Published
2025-05-01
DOI
https://doi.org/10.1177/20503121251360090
Citations
2
Primary Topic
Burn Injury Management and Outcomes
Type
article
Field-Weighted Citation Impact
2.71
Controls
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article

Pediatric BurnNet: Robust multi-class segmentation and severity recognition under real-world imaging conditions

Zhen Liu, Xiang Li, Lei Liu
2 citations
SAGE Open Medicine
Burn Injury Management and Outcomes
2.71
article

Pediatric BurnNet: Robust multi-class segmentation and severity recognition under real-world imaging conditions

Zhen Liu, Xiang Li, Lei Liu
article en
2 citations

Abstract

Dfusion provides accurate, end-to-end segmentation and depth grading of pediatric burn wounds captured in uncontrolled environments. Its robust performance and modest computational demand support deployment on mobile devices, offering rapid, objective assistance for clinicians in resource-limited settings and enabling more precise triage and treatment planning for pediatric burn care.

SAGE Open MedicineVol. 13
Beijing Children’s Hospital (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
Burn Injury Management and Outcomes
2.71
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