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基于融合的分段辅助分类用于自动化的CXR图像分析

Shilu Kang¹, Dongfang Li¹, Jiaxin Xu¹

¹Information Engineering College, Henan University of Science and Technology, Luoyang 471000, China.

Sensors (Basel, Switzerland)

|August 14, 2025

概括

一种新的细分辅助方法通过使用部分卷积细分网络 (PCSNet) 准确地细分肺部领域,并将结果与原始图像合并以改善肺部疾病的诊断,从而改善胸部X射线分类.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

胸部X射线 (CXR) 的准确分类对于诊断肺部疾病至关重要.
现有的深度学习模型很难在CXR图像中区分非肺部特征.

研究的目的:

为CXR图像提出一种基于聚变的新型细分辅助分类方法.
通过深度学习提高肺部疾病诊断的准确性和效率.

主要方法:

使用编码器-解码器架构开发了一种轻量级的细分模型,即部分卷积细分网络 (PCSNet).
PCSNet从CXR图像中生成肺口罩,然后将其与原始图像合并.
在融合图像上使用改进的轻量级ShuffleNetV2模型进行分类.

主要成果:

在CXR数据集 (MC,SH) 上,PCSNet表现出高的细分性能,优于其他七种模型.
与注意力网相比,PCSNet实现了更高的精度 (98.94%) 和边界精度 (97.86%),参数减少了62%.
拟议的方法在CXIP数据集上提高了肺炎分类准确率0.14% (98.55%),在COVIDx数据集上提高了COVID-19分类准确率0.1% (97.50%),显著提高了特异性.

结论:

细分辅助的融合方法有效地提高了CXR分类的准确性.
基于医疗图像进行肺病诊断,PCSNet提供了一个计算效率高,准确度高的解决方案.
提出的方法显示了临床上比医学图像分析中最先进的方法有意义的改进.

关键词:

胸部X射线胸部X射线图像的分类图像的分类.轻量级的模型轻量级的模型.肺部疾病肺部疾病细分模型的细分模型.

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