深度学习技术的应用,用于检测肺胸在胸部X射线影像中的肺胸部
Lawrence Y Deng1, Xiang-Yann Lim2, Tang-Yun Luo3
1Department of Artificial Intelligence, Tamkang University, Tamsui, New Taipei City 251301, Taiwan.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
机器学习模型可以从X射线图像中可靠地检测肺胸部,帮助医疗专业人员. 一个推的模型提供了高准确度和低假阳性率,提高了诊断效率.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 机器学习用于疾病检测和检测
背景情况:
- 医学图像分析,特别是肺部疾病如肺胸炎,面临着由于高工作量和资源紧张的挑战.
- 机器学习 (ML) 为自动化图像解释和提高诊断效率提供了潜在的解决方案.
研究的目的:
- 开发和评估可靠的ML系统,从X射线图像中检测肺胸部.
- 为了确定临床应用的最佳模型和超参数配置,优先考虑准确性并最大限度地减少假阳性.
主要方法:
- 采集了8000多张X射线图像,其中包括1000多张来自肺胸病患者的图像,与Poh-Ai医院合作.
- 在胸部X射线数据集上对转移学习的多个ML模型和超参数设置进行了比较.
- 基于包括精度,假阳性率和假阴性率在内的指标评估模型性能,并结合临床反.
主要成果:
- 最好的转移学习模型实现了平均精度 (AP) 51.57和AP75的61.40,精度为93.89%,假阳性为1.12%,假阴性为4.99%.
- 一种专门推用于临床使用的模型以95.68%的准确性表现出卓越的性能,错误阳性率明显降低,为0.88%.
- 临床反突出了对最小化假阳性模型的偏好,与推模型的特征保持一致.
结论:
- 开发的ML系统显示了在X射线图像中可靠检测肺胸部的可行性.
- 推的模型,其高精度和低假阳性率,适合整合到医院的工作流程.
- 这种方法有可能扩展到检测其他疾病,并在多个医疗机构中部署.
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