机器学习启用诊断,在胸部X射线图像中改善疾病病变的可视化
Md Fashiar Rahman1, Tzu-Liang Bill Tseng1, Michael Pokojovy2
1Department of Industrial, Manufacturing and Systems Engineering, The University of Texas, El Paso, TX 79968, USA.
Diagnostics (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种可解释的AI方法,使用多层梯度类激活映射 (ML-Grad-CAM) 来改进胸部X射线 (CXR) 中的COVID-19和肺炎检测. 该方法实现了高精度,并提供了对疾病严重程度的视觉见解.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 班级激活地图 (CAM) 有助于理解医学成像中的卷积神经网络 (CNN) 决策.
- 对于胸部X射线 (CXR) 分析的现有深度学习模型通常缺乏通过突出性地图的解释性.
- 准确诊断CXRs的COVID-19和肺炎对于患者护理至关重要.
研究的目的:
- 开发一种可解释的深度学习模型,用于从CXRs分类COVID-19,肺炎和正常病例.
- 提高分类准确性,并提供诊断结果的可视解释性.
- 通过突出性地图来评估感染严重性的量化措施.
主要方法:
- 一个基于VGG-16的深度学习模型,包含图像增强,感兴趣区域 (ROI) 裁剪和数据增强.
- 集成多层梯度类激活映射 (ML-Grad-CAM) 算法,用于生成特定类的突出性图.
- 从ML-Grad-CAM输出的严重性评估指数 (SAI) 的定义和计算.
主要成果:
- 该模型在分类COVID-19,肺炎和正常CXR方面实现了96.44%的准确性.
- ML-Grad-CAM生成了详细的突出地图,与标准Grad-CAM相比提供了更好的可视化.
- 严重性评估指数 (SAI) 提供了感染严重性的定量衡量.
结论:
- 拟议的可解释AI方法显著提高了呼吸道疾病的CXR分类准确性.
- ML-Grad-CAM为医疗从业者提供了有价值的视觉见解,有助于诊断和严重程度评估.
- 这项研究弥合了医学图像分析,特别是COVID-19和肺炎检测方面的可解释AI的差距.
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