可解释的COVID-19胸部X射线检测基于手工制作的特征分析和顺序神经网络
Rukundo Prince1, Zhendong Niu1, Zahid Younas Khan2
1Beijing institute of technology, China.
Computers in biology and medicine
|January 23, 2025
概括
这项研究引入了新的算法,即动态共发生灰色水平矩阵 (DC-GLM) 和上下文适应多尺度Gabor网络 (CAMSGNeT),用于在胸部X射线中改进COVID-19检测. 这些方法提供了更高的准确性和可解释性,同时也是资源高效的.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算病理学计算病理学
背景情况:
- 深度学习增强了在胸部X射线中检测COVID-19的医疗图像分析.
- 现有的方法面临挑战,包括有限的解释性,高计算成本和数据要求.
研究的目的:
- 引入新的算法,DC-GLM和CAMSGNeT,以解决COVID-19检测深度学习的局限性.
- 提高医疗图像分析中的诊断精度,可解释性和资源效率.
主要方法:
- 开发了动态协同发生的灰色水平矩阵 (DC-GLM) 来捕捉COVID-19的关键指标,如地面玻璃的不透明度,整合和纤维化.
- 引入了上下文适应多尺度Gabor网络 (CAMSGNeT),用于使用上下文适应扩散增强细特征提取.
- 使用简单的优化顺序神经网络进行分类和特征重要性分析以实现可解释性.
主要成果:
- DC-GLM有效地捕捉了胸部X射线中的粗纹理模式和空间相关性.
- CAMSGNeT保留了细节,并增强了边缘/纹理特征,保持了像空气支气管图一样的图案.
- 综合方法实现了高分类准确性,在两个不同的数据集上达到98.27%和100%.
结论:
- 拟议的DC-GLM和CAMSGNeT算法为COVID-19检测提供了一个更易于解释,更精确,更节省资源的解决方案.
- 特性重要性分析提高了模型的透明度,有助于临床决策.
- 这种方法代表了人工智能驱动的医疗图像分析用于传染病检测的重大进步.
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