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用两种主要计算机断层扫描模式的深度学习方法评估间歇性肺部疾病

Hüseyin Alper Kiziloğlu¹, Emrah Çevik², Kenan Zengin²

¹Department of Radiology, Faculty of Medicine, Tokat Gaziosmanpaşa University, Tokat, Türkiye.

Current medical imaging

|February 28, 2024

概括

深度学习模型从高分辨率计算机断层扫描 (HRCT) 中准确诊断了间歇性肺病 (ILD) 模式. VGG19模型实现了98.05%的准确性,显示AI.

科学领域:

医疗成像中的人工智能
放射学和诊断成像放射学和诊断成像
计算病理学计算病理学

背景情况:

间歇性肺部疾病 (ILD) 是一组导致肺炎和纤维化的疾病.
高分辨率计算机断层扫描 (HRCT) 揭示了不同的ILD模式.
准确的ILD模式差异诊断对于患者管理至关重要.

研究的目的:

开发和评估深度学习模型,用于对常见ILD模式的差异诊断.
在ILD分类中评估VGG16和VGG19架构的诊断准确性.

主要方法:

患者被分为三组:常见间歇性肺炎 (UIP),非特异性间歇性肺炎 (NSIP) 和正常的肺膜.
使用了VGG16和VGG19的深度学习架构.
85%的数据用于训练人工智能模型,其余的15%用于没有人类干预的测试.

主要成果:

在测试数据集上,VGG16模型在诊断ILD模式方面实现了95.02%的准确性.
VGG19模型的准确性更高,在测试数据集上达到98.05%.
这两种模型都在区分UIP,NSIP和正常的肺膜膜瘤方面表现出很高的表现.

结论:

深度学习模型,特别是VGG19,在区分常见的ILD模式方面表现出很高的准确性.
基于人工智能的方法显示出有很大的潜力,可以帮助放射科医生在ILD诊断中.
这些发现支持使用深度学习来自动分析ILD中的HRCT扫描.

关键词:

人工智能的人工智能是人工智能.计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习. 深度学习.间歇性肺炎是一种肺炎.这是NSIP的NSIP.在UIP中,UIP是UIP.

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