RNGU-NET:使用胸部X射线图像对结核病进行细分的新型高效方法
1Computer Engineering/Faculty of Engineering and Architecture, Kirikkale University, Kirikkale, Turkey.
PeerJ. Computer science
|March 4, 2024
概括
一个新的深度学习模型RNGU-NET显著提高了结核病细分的准确性. 这种方法增强了医疗图像中的检测,优于现有的U-NET模型,以获得更好的患者结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 结核病 (TB) 是一个重要的全球健康问题,影响肺,和大脑等重要器官.
- 世界卫生组织 (WHO) 报告称,在2020年,约有1000万名结核病感染者.
- 在医学图像中精确细分结核病对于诊断和治疗至关重要.
研究的目的:
- 引入和评估一个新的深度学习架构,RNGU-NET,以改善结核病细分.
- 解决医疗图像细分现有基于U-Net模型的局限性.
- 为了比较RNGU-NET与U-NET和U-NET+ResNet的性能.
主要方法:
- 开发RNGU-NET架构,集成ResNet,非本地区块和门注意区块.
- 使用ResNet增强编码器和使用Gate Attention Block解码器.
- 引入本地非本地区块,以缓解U-Net模型中的瓶问题.
- 使用深数据集进行结核细分的比较分析.
主要成果:
- 在结核病细分方面,RNGU-NET取得了卓越的表现.
- 拟议的RNGU-NET模型的准确率为98.56%,子系数为97.21%,贾卡德指数为96.87%.
- 与RNGU-NET.NET相比,U-NET和U-NET+ResNet的性能指标较低.
结论:
- RNGU-NET架构代表了结核病细分的重大进步.
- 新的架构组件有效地克服了以前的细分模型的局限性.
- 在诊断和监测结核病方面,RNGU-NET具有很高的临床应用潜力.
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