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Updated: Jan 21, 2026

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Protocol to Create Chronic Wounds in Diabetic Mice
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整合性图像处理框架,用于增强检测麻风相关的慢性伤口
Jalpa Mehta1, Saraswati Mishra1, Rashmi Nilesh Malvankar1
1Mahavir Education Trust Chowk, W.T Patil Marg, D P Rd, next to Duke's Company, Chembur, Mumbai, Maharashtra.
The international journal of lower extremity wounds
|January 19, 2026
概括
CO-WinF框架使用智能手机图像改进了使用智能手机图像的自动化长期伤口分析. 它提高了图像质量,细分精度和功能融合,以在资源有限的环境中提供更好的诊断支持.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 皮肤病学 皮肤病学
背景情况:
- 从智能手机图像中自动化慢性伤口分析面临着诸如照明不良和病变边缘模糊等挑战.
- 准确的细分和特征提取对于可靠的诊断支持至关重要.
研究的目的:
- 引入CO-WinF框架,用于强大的自动化慢性病伤口分析.
- 为了提高图像质量,细分精度和功能融合,以提高诊断能力.
主要方法:
- CO-WinF框架整合了三个模块:AINCE用于对比重绘和纹理恢复,MEPS用于多分辨率编码和使用渐变驱动的注意力进行边缘感知细分,HSTFC用于注意力加权的深度和手工制作特征的融合.
- 为最终分类使用了针对类不平衡进行优化的渐变增强集体分类器.
主要成果:
- 在CO2Wounds-V2数据集上,CO-WinF实现了高性能,精度为94.98%,回忆率为95.78%,F1评分为93.10%,IOU值为87.09%.
- 与现有的最先进的方法相比,该框架显示出更优异的结果.
结论:
- CO-WinF为基于智能手机的伤口分析提供了一个计算效率高和强大的管道.
- 该框架提供可解释的诊断支持,为资源有限的临床环境建立了一个新的基准.
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