使用卷积神经网络与MLP-Mixer进行活跃和非活跃结核病分类
Beanbonyka Rim1, Hyeonung Jang2, Hongchang Lee2
1Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.
Bioengineering (Basel, Switzerland)
|June 26, 2025
概括
这项研究引入了用于早期肺结核检测的深度学习模型. 该模型能够准确地区分活跃结核病和非活跃结核病,有助于临床决策和查.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 早期发现结核病 (TB) 对于有效治疗和预防重新激活至关重要.
- 识别不活跃的结核病形式,如潜伏或治愈的结核病,对于主动管理至关重要.
- 传统的诊断方法在快速准确地区分结核病状态方面可能存在局限性.
研究的目的:
- 开发和评估一种深度学习模型,用于活跃与非活跃结核病例的二元分类.
- 评估模型在区分不同形式的结核病方面的表现,以改善早期检测.
- 提供一个工具,支持结核病查和管理的临床决策.
主要方法:
- 使用EfficientNet骨干和MLP-Mixer分类头部开发一个深度学习二元分类模型.
- 在Cheonan Soonchunhyang医院的数据集上微调模型.
- 使用Noisy Student培训方法,在JFT-300M数据集上预先训练过权重的转移学习的应用.
主要成果:
- 深度学习模型在测试组中实现了高性能.
- 获得了96.3%的精度,95.9%的灵敏度和96.6%的特异性.
- 与传统模型相比,在区分活跃结核病和非活跃结核病方面表现出具有竞争力的结果.
结论:
- 开发的深度学习模型显示了支持结核病诊断中的临床决策的巨大潜力.
- 该模型可以简化早期查工作流程,特别是潜在结核病.
- 这种人工智能驱动的方法在早期识别和治疗结核病方面提供了有前途的进展.
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