可解释的深度神经网络支持的方案用于从胸部X射线影像检测结核病
B Uma Maheswari1, Dahlia Sam2, Nitin Mittal3
1Department of Computer Science and Engineering, St. Joseph's College of Engineering, OMR, Chennai, Tamilnadu, 600119, India.
BMC medical imaging
|February 5, 2024
概括
这项研究引入了一个浅层卷积神经网络 (CNN) 来从胸部X射线进行结核查,实现高精度. 该模型为传统诊断方法提供了更快,更客观的替代方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 结核病的诊断依赖于胸部放射,这是耗时和主观的.
- 机器学习为改善医学诊断提供了潜力,包括结核病查.
研究的目的:
- 开发一个浅层卷积神经网络 (CNN) 来通过胸部X射线高效准确地查结核病.
- 加强诊断解释,减少结核病检测中的主观性.
主要方法:
- 一个浅的CNN与四个卷积-maxpooling层被设计.
- 使用贝叶斯优化优化了超参数.
- 用准确度,F1得分,灵敏度,特异性和ROC AUC来评估模型性能.
- 使用类激活地图 (CAM) 和局部可解释模型-不可知解释 (LIME) 评估了可解释性.
主要成果:
- 浅层的CNN实现了0.95.95的最高分类准确度,F1得分,灵敏度和特异性.
- 接收器操作特征 (ROC) 曲线显示曲线下的峰值面积 (AUC) 为0.976.
- 该模型的透明度和可解释性与最新的Dense.Net相比进行了评估.
结论:
- 开发的浅CNN提供了一个高度准确和潜在的更客观的方法,用于从胸部X射线查结核病.
- 该模型的可解释性特征有助于其临床实用性和可信度.
- 这种方法为传统的诊断方法提供了有希望的替代方案,解决了时间和主观性的局限性.
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