一个短暂的SE-Relation基于网络的电子鼻子,用于区分COPD
Zhuoheng Xie1, Yao Tian2,3, Pengfei Jia4,5
1School of Mechanical Electrical and Information Engineering, Shandong University, Weihai 264209, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
我们开发了一种先进的电子鼻子,用于使用有限的呼吸样本诊断慢性阻塞性肺病 (COPD). 我们的SE-RelationNet模型实现了高精度,使得早期检测,并可能降低肺癌风险.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性阻塞性肺病 (COPD) 的诊断通常需要多次测试.
- 早期发现COPD对于及时干预和降低风险至关重要.
- 有限的呼吸样本对准确的诊断模型构成了挑战.
研究的目的:
- 开发用于COPD诊断的先进电子鼻子系统.
- 用有限的呼吸样本来评估SE-RelationNet的性能.
- 用最小的数据证明早期COPD检测的可行性.
主要方法:
- 使用SE-RelationNet,集成剩余块,BiGRU层和挤压激发注意力.
- 雇佣了一些射击学习任务 (一射击和几射击) 用于模型评估.
- 进行了废弃性研究以验证模型组件和稳定性.
主要成果:
- 在 4-way 1-shot 任务中达到 85.8% 的平均精度 (F1-score = 0.852).
- 达到93.3%的准确性 (F1分数 = 0.931) 每个类别有四个样本.
- 在一次性任务中,SE-RelationNet的表现超过了SiameseNet和ProtoNet的15%.
结论:
- 拟议的电子鼻子有效地通过最小的呼吸样本来诊断COPD.
- SE-RelationNet提供了一个强大而准确的解决方案,用于几次射击的COPD检测.
- 这项技术有助于早期诊断COPD,可能降低肺癌风险.
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