Eff-ReLU-Net:一个深度学习框架,用于多类伤口分类
Sifat Ullah1, Ali Javed1, Muteb Aljasem2
1Department of Software Engineering, University of Engineering and Technology-Taxila, Taxila, 47050, Pakistan.
BMC medical imaging
|July 2, 2025
概括
一个新的深度学习模型,Eff-ReLU-Net,准确地分类慢性伤口,改善患者护理. 这种自动化的伤口分类系统提高了医疗保健专业人员的诊断可靠性和效率.
科学领域:
- 医学技术 医学技术 医学技术
- 医疗保健中的人工智能
- 计算生物学是一种计算生物学.
背景情况:
- 慢性伤口对健康构成重大风险,包括感染和截肢.
- 越来越多的患病率需要自动伤口评估,以减少对手工方法的依赖.
- 准确和快速的伤口分类对于有效的治疗至关重要.
研究的目的:
- 开发一种高效可靠的深度学习模型,用于多类慢性伤口分类.
- 改进现有的EfficientNet-B0架构,以进行增强的功能提取.
- 在各种伤口数据集上验证模型的性能.
主要方法:
- 提出了Eff-ReLU-Net,这是一个基于EfficientNet-B0的模型,包含ReLU激活和额外的密集层.
- 采用数据增强技术,包括旋转和翻译,以增强模型概括性.
- 在AZH和Medetec伤口数据集上评估模型性能,并进行跨体分析.
主要成果:
- 在这两个数据集上,Eff-ReLU-Net实现了高性能指标.
- 在Medetec数据集上获得了92.33%的准确性,97.66%的精度,95.33%的回忆率和96.48%的F1分数.
- 在AZH数据集上获得了90%的准确性,89.45%的精度,92.19%的回忆率和90.84%的F1分数.
结论:
- 拟议的Eff-ReLU-Net在分类慢性伤口方面表现出显著的有效性.
- 该模型的架构和增强策略有助于强大的性能和通用性.
- 使用Eff-ReLU-Net的自动伤口分类为临床实践提供了可靠的解决方案.
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