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Burn Injuries

Burn Injuries

Burn injuries occur when the skin and underlying tissues are damaged due to exposure to heat, electricity, chemicals, radiation, or friction. They can vary in severity, from minor superficial burns to severe deep burns that can be life-threatening.
The damage results in the death of skin cells, which can lead to a massive loss of fluid. Dehydration, electrolyte imbalance, and renal and circulatory failure follow, which can be fatal. Burn patients are treated with intravenous fluids to offset...

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Updated: Jul 20, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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基于空间注意力的残留网络用于人类烧伤的识别和分类.

D P Yadav¹, Turki Aljrees², Deepak Kumar³

¹Department of Computer Engineering and Applications, GLA University, Mathura, India.

Scientific reports

|August 2, 2023

概括

这项研究介绍了BuRnGANeXt50,这是一个改进的深度学习模型,用于诊断人体烧伤. 该模型提高了分类烧伤程度和深度的准确性,有助于更快地对患者进行查.

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科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

手动烧伤诊断是复杂和耗时的.
现有的机器学习 (ML) 和深度学习 (DL) 模型用于烧伤诊断具有局限性,包括依赖手工制作的功能或在设计强大的模型时面临的挑战.
浅层DL方法往往缺乏远程特征依赖性,影响诊断效率.

研究的目的:

开发和评估基于注意力的深卷积神经网络 (CNN) 模型,用于准确的人类烧伤诊断.
通过解决特征依赖性和提高燃烧程度和深度的分类准确性来改进现有的DL模型.

主要方法:

实施并比较了几种深度CNN模型 (ResNeXt,VGG16,AlexNet) 用于烧伤诊断.
开发了一个基于注意力的模型,BuRnGANeXt50,它将特征地图分为类别,以突出道依赖性,并使用空间注意力地图.
为烧伤诊断优化了BuRnGANeXt50的内核和卷积层,并对其在Burns_BIP_US_database上的性能进行了评估.

主要成果:

以前的浅CNN模型显示的结果不太可靠,需要改进注意力机制.
提出的BuRnGANeXt50模型实现了高灵敏度:97.22%用于分类燃烧程度和99.14%用于分类燃烧深度.
该模型通过注意力机制证明了通过注意力机制改善的特征依赖性保护.

结论:

开发的BuRnGANeXt50模型为人类烧伤诊断提供了可靠和高效的解决方案.
该模型可以促进燃烧患者的快速查,并可在云端或本地系统上部署.
该研究强调了注意力模块在医学图像分析的深度学习中的重要性,并提供了可重现的代码.