使用胸部X射线成像检测肺部疾病的深度卷积神经网络模型.
1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.
Pulmonary medicine
|July 2, 2025
概括
一个自动化系统有效地检测肺炎和COVID-19使用胸部X射线和CT扫描. 这种深度学习模型实现了高精度,有助于早期肺部疾病诊断.
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:
- 肺炎和COVID-19等肺部疾病对全球健康构成重大挑战.
- 早期和准确的诊断对于有效的患者管理和治疗至关重要.
- 医学成像,特别是胸部X射线,是肺部疾病检测的基石,由于可访问性和速度.
研究的目的:
- 开发一种自动化系统,在医学扫描中检测多种肺部疾病,包括肺炎和COVID-19.
- 利用定制的卷积神经网络 (CNN) 与预训练模型和图像增强集成,以提高诊断准确度.
- 为了评估系统在多样化的胸部X射线和CT图像数据集上的性能.
主要方法:
- 利用了6400张胸部X射线和CT图像的数据集,分为肺炎,COVID-19和正常类别.
- 采用数据增强技术来解决数据集中的类不平衡问题.
- 开发了一个深度学习模型,包括定制的CNN,预训练模型,图像增强,预处理和分类阶段.
主要成果:
- 自动化系统实现了高性能指标:96%的精度,95.33%的回忆,95.66%的F1得分和97.24%的准确性.
- 与其他现有的深度学习模型相比,在肺部疾病检测方面表现出更高的有效性.
- 美国有线电视新闻网 (CNN) 的综合方法,预先训练的模型和图像增强证明是非常成功的.
结论:
- 拟议的自动化系统对多种肺部疾病的准确和高效检测具有显著的前景.
- 这种人工智能驱动的方法可以提高早期诊断,潜在地改善患者的治疗结果和疾病管理.
- 该研究强调了定制CNN和图像增强在医学诊断中的潜力.
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