September 27, 2025Open Access

WITHDRAWN: Optimizing Burn Wound Segmentation with Advanced Boundary Detection via Deep Learning and Multi-Scale Techniques

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Optimizing burn wound segmentation enhances accuracy in medical imaging and treatment planning.
Advanced boundary detection methods using deep learning achieved improved results compared to traditional techniques.
The application of multi-scale techniques allows for better recognition of complex wound boundaries.
Ensuring the integrity of the research is vital for maintaining ethical standards in medical research.

Implication

Analysis reveals optimized segmentation of burn wounds using deep learning and multi-scale techniques, suggesting improvements in medical imaging processes.

Abstract

Abstract 26 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform.

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Cite This Study

Dattatreya et al. (2025) studied this question.

synapsesocial.com/papers/68d7cc6aeebfec0fc5238b6f https://doi.org/10.21203/rs.3.rs-7412711/v2

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Implication

Analysis reveals optimized segmentation of burn wounds using deep learning and multi-scale techniques, suggesting improvements in medical imaging processes.

WITHDRAWN: Optimizing Burn Wound Segmentation with Advanced Boundary Detection via Deep Learning and Multi-Scale Techniques

Key Points

Implication

Abstract

Discussion

Cite This Study

WITHDRAWN: Optimizing Burn Wound Segmentation with Advanced Boundary Detection via Deep Learning and Multi-Scale Techniques

Key Points

Implication

Abstract

Discussion

Cite This Study

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