计算机辅助诊断使用嵌入式集体深度学习进行多类耐药结核病分类
Kanchana Sethanan1, Rapeepan Pitakaso2, Thanatkij Srichok2
1Department of Industrial Engineer, Faculty of Engineering, Research Unit on System Modelling for Industry, Khon Kaen University, Khon Kaen, Thailand.
Frontiers in medicine
|July 13, 2023
概括
一个新的网络应用程序TB-DRD-CXR使用深度学习来根据耐药性对结核病患者进行分类. 它实现了高精度和效率,优于现有的药物敏感性和耐药性结核病分类方法.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 结核病 (TB) 构成了重大的全球卫生挑战,耐药性使治疗复杂化并增加死亡率.
- 精确的结核病菌株分类,包括药物敏感结核病 (DS-TB) 和各种形式的耐药结核病 (DR-TB),如多抗药性结核病 (MDR-TB),先前广泛耐药性结核病 (pre-XDR-TB) 和广泛耐药性结核病 (XDR-TB),对于有效的患者管理至关重要.
- 现有的诊断方法可能缺乏快速临床决策所需的速度和准确性.
研究的目的:
- 开发和评估TB-DRD-CXR网络应用程序,用于将结核病患者分类为耐药性亚组.
- 实施一个集体深度学习模型,能够区分五种TB亚型:DS-TB,DR-TB,MDR-TB,XDR-TB前和XDR-TB.
主要方法:
- 开发了一个集体深度学习模型,结合了新的融合技术,图像细分,数据增强和学习率策略.
- 该模型的性能与最先进的技术和标准的卷积神经网络 (CNN) 架构进行了基准测试.
- 开发了TB-DRD-CXR应用程序,并与医疗人员进行了可用性测试.
主要成果:
- 拟议的集体深度学习模型在现有方法中表现出优异的性能,精度增加范围从4.0%到33.9%.
- 与标准CNN模型 (例如,DenseNet201,NASNetMobile,EfficientNetV2B3) 相比,观察到显著的准确性改进,收益高达93.4%.
- 结核病-DRD-CXR应用实现了96.7%的高准确率,卓越的基于时间的效率 (每分钟4.16个目标) 和整体相对效率 (100%).
结论:
- 结核病-DRD-CXR网络应用程序有效地根据耐药性水平对结核病患者进行分类,使用先进的集体深度学习模型.
- 该系统具有很高的准确性,效率和用户满意度 (SUS得分为96.7%),这表明它有临床采用潜力.
- 开发的应用程序代表了结核病诊断的重大进步,比目前的方法提供了更好的性能.
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