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一个高效的基于深度学习的形态意识到结核查特征的层次混合,使用胸部X射线图像的细分进行结核查

Mashael Maashi¹, Huda Eltayeb², Mashael M Asiri³

¹Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh, 11543, Saudi Arabia.

Scientific reports

|November 27, 2025

概括

这项研究引入了一种高效的深度学习模型,用于精确的结核病 (TB) 查和使用胸部X射线 (CXR) 的肺部细分. 该模型达到98.33%的准确性,有助于早期诊断和临床决策.

科学领域:

医疗成像医学成像
人工智能的人工智能
肺部病理学肺部病理学

背景情况:

结核病 (TB) 是死亡的主要原因,通过胸部X射线 (CXRs) 早期检测至关重要.
使用CXR的肺癌查有效降低死亡率.
准确的结核病鉴定对于患者的治疗结果和预防传播至关重要.

研究的目的:

开发一个自动化的深度学习 (DL) 框架,用于精确的肺部细分和使用CXR图像进行结核病查.
通过先进的图像分析来支持早期诊断和临床决策.
为了提高从放射图像中识别肺部异常的准确性.

主要方法:

开发了一种有效的基于深度学习的层次特征融合方法,用于肺部细分和结核病查 (EDLHFFA-LSTS) 模型.
图像预处理包括大小调整,自适应过 (AF) 和直方图平衡 (HE).
该模型使用Res-UNet进行细分,融合了EfficientNetV2,CapsNet和卷积视觉变压器 (CViT) 来进行特征提取,并使用堆叠自动编码器 (SAE) 来进行分类.

主要成果:

该EDLHFFA-LSTS模型在CXR口罩和标签数据集上表现出有希望的结果.
与现有模型相比,模拟显示出更高的性能.
该方法在结核病查和肺部细分方面实现了98.33%的高精度.

结论:

开发的基于DL的框架为自动肺部细分和结核病查提供了有效的解决方案.
EDLHFFA-LSTS模型显著提高了从CXRs检测结核病的诊断准确性.
这种方法支持放射科医生在肺部疾病的早期诊断和临床决策.

关键词:

胸部X射线图像胸部X射线图像功能提取功能提取肺部细分的细分肺部的细分堆叠的自动编码器对结核病进行查.

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