GL-FusionNet:将全球和地方特征融合在一起,以分类深层和表面部分厚度烧伤
Zhiwei Li1, Jie Huang2, Xirui Tong2
1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
深度学习使用GL-FusionNet准确地分类燃烧深度,融合了本地和全球特征. 这种自动化方法有助于临床医生区分表面部分厚度烧伤和深部部分厚度烧伤,提高诊断效率.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 评估烧伤伤口的伤口.
背景情况:
- 烧伤是常见的,痛苦的伤害,需要准确的深度分类.
- 对于没有经验的临床医生来说,区分表面部分厚度和深部部分厚度烧伤是很有挑战性的.
- 需要自动和准确的烧伤深度分类来改善患者护理.
研究的目的:
- 开发一个自动化的深度学习模型,用于准确的燃烧深度分类.
- 提高在临床环境中评估烧伤伤口的效率和准确性.
- 为了区分表面部分厚度和深层部分厚度烧伤.
主要方法:
- 利用U-Net进行烧伤伤口细分,获得高的Dice和IoU分数.
- 提出了GL-FusionNet,这是一个新的分类模型,将本地特征 (ResNet50) 和全球特征 (ResNet101) 融合在一起.
- 员工临床收集,医生标记的烧伤图像用于培训和验证.
主要成果:
- 实现U-Net细分,Dice得分为85.352分,IoU得分为83.916分.
- 在燃烧深度分类方面,GL-FusionNet模型表现出卓越的性能.
- 实现了高分类指标:93.523%的准确性,93.67%的回忆,93.51%的精度,93.513%的F1分数.
结论:
- 拟议的深度学习方法,GL-FusionNet,为燃烧深度分类提供了准确和自动化的解决方案.
- 这种方法显著提高了初始烧伤诊断和护理护理的效率.
- 该模型显示了广泛临床采用的潜力,以帮助医疗人员评估烧伤.
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