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胸部X射线的自动分类:使用注意力机制的深度学习方法

Burcu Oltu¹, Selda Güney², Seniha Esen Yuksel³

¹Department of Biomedical Engineering, Baskent University, Etimesgut, Ankara, 06790, Türkiye. boltu@baskent.edu.tr.

BMC medical imaging

|March 4, 2025

概括

这项研究引入了一种先进的深度学习模型,用于通过胸部X射线 (CXR) 来诊断肺部疾病,在检测COVID-19,肺炎和肺部不透明度方面达到高精度.

科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
肺部疾病的诊断肺部疾病的诊断

背景情况:

像COVID-19和肺炎这样的肺部疾病需要快速诊断.
胸部X射线 (CXR) 是检测这些疾病的常见,具有成本效益的工具.
准确解释CXR是一个重大挑战.

研究的目的:

开发和验证深度学习 (DL) 模型,用于基于CXR的自动诊断.
为了提高检测COVID-19的准确性和效率,肺部不透明性和病毒性肺炎.
在临床环境中建立CXR解释的新基准.

主要方法:

开发了一个结合Vision Transformer (ViT) 和DenseNet201的深度学习框架.
该模型使用全球平均聚合 (GAP) 来增强功能保留.
用于培训和验证,使用了21,165个CXR的数据集.

主要成果:

该模型实现了COVID-19的99.4%准确率和病毒性肺炎的99.63%.
肺度 (96.45%) 和正常受试者 (95.97%) 的高精度也被观察到.
总体精度达到了97.87%,超过了现有的最先进的方法.

结论:

拟议的DL框架在通过CXR诊断肺部疾病方面表现出卓越的性能.
五次交叉验证确保了模型的稳定性和可靠的性能评估.
集成的Grad-CAM可视化增强了模型的解释性,为诊断决策提供了洞察力.

关键词:

胸部X射线 (CXR) 是指胸部X射线.卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.视觉变压器 (ViT) 是一个视觉变压器.

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