通过深度学习在胸部X射线中通过计算机辅助诊断,通过深度学习对胸部X射线的肺流进行自动诊断
Ya-Yun Huang1, Yu-Ching Lin2,3,4, Sung-Hsin Tsai5
1Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan.
Diagnostics (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种自动化系统,用于使用深度学习在胸部X射线 (CXR) 上检测肺溢液. 该系统实现了93.27%的准确性,大大改善了以前的方法,并帮助医生进行诊断.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 肺部医学 肺部医学
背景情况:
- 胸腔溢液是一种常见的肺部疾病,如果不治疗,可能导致严重的并发症.
- 对胸部X射线 (CXR) 图像进行手动解读,以检测肺部溢液是耗时的.
- 需要自动检测系统,以提高诊断流的效率和准确性.
研究的目的:
- 开发和评估一种用于检测CXR图像中的肺流的自动化系统.
- 通过使用人工智能,提高胸腔溢出诊断的效率和准确性.
主要方法:
- 整合了图像裁剪,图像增强和EfficientNet-B0深度学习模型.
- 图像裁剪侧重于从心脏到腰角的区域.
- 图像增强技术被应用,以强调膜溢出特征,以改善模型学习.
主要成果:
- 拟议的图像增强将模型识别精度提高了约4.33%.
- 该自动化系统实现了93.27%的最终精度,用于肺部溢液检测.
- 与之前的研究结果 (77.00%) 相比,这代表了21.30%的显著改善.
结论:
- 开发的系统可以作为医生有效的辅助诊断工具.
- 它提供了标准化的检测结果,减少了手动翻译的工作量,并提高了肺部护理效率.
- 该系统在自动化流检测方面取得了重大进展.
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