通过Grad-CAM镜头解释静脉和动脉的图像:基于CNN的伤口图像分类中的洞察和影响
Hannah Neuwieser1, Naga Venkata Sai Jitin Jami2,3, Robert Johannes Meier4
1Department of Plastic, Hand, and Reconstructive Surgery, University Hospital Regensburg, 93053 Regensburg, Germany.
Diagnostics (Basel, Switzerland)
|September 13, 2025
概括
深度学习模型准确地区分动脉和静脉,有助于诊断慢性下肢伤口. 这种人工智能方法提高了复杂伤口护理的诊断效率.
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
- 伤口诊断 伤口诊断 伤口诊断
背景情况:
- 慢性下肢伤害,包括动脉和静脉,带来了重大的临床和经济挑战.
- 准确的差异诊断对于有效治疗这些复杂的伤口至关重要.
研究的目的:
- 评估先进的深度学习模型来对动脉与静脉的分类.
- 使用Grad-CAM可视化这些AI模型的决策过程.
主要方法:
- 对607张下肢图像 (198张动脉,409张静脉) 的回顾性分析.
- 培训和评价五个卷积神经网络:ResNet50,ResNeXt50,ConvNeXt,EfficientNetB2和EfficientNetV2.2的使用情况.
- 性能指标包括准确性,精度,回忆,F1得分和ROC-AUC; 视觉化的Grad-CAM.
主要成果:
- 在各种模型中观察到高分类性能,准确度从72%到98%不等 (ResNeXt50).
- 通过精确度和回忆指标证明了动脉和静脉之间的强烈区别.
- EfficientNetV2实现了最高的精度,表明了强大的分类能力.
结论:
- 人工智能辅助的分类为提高诊断效率,提高分辨静脉和动脉的诊断效率提供了宝贵的工具.
- 深度学习模型在支持慢性伤口管理的临床决策方面表现有前途.
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