使用Xception网络架构有效检测Covid-19:使用X射线图像进行技术调查
Kuljeet Singh1, Surbhi Gupta2, Neeraj Mohan3
1Department of Computer Science, School of Sciences, Christ University, Delhi-NCR, India.
Health informatics journal
|July 29, 2025
概括
这项研究引入了一种AI模型,用于使用胸部X射线诊断COVID-19. 先进的Xception模型实现了99.94%的准确性,有助于快速有效地检测疾病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 随着COVID-19的流行,人们在医疗保健和教育领域的关键需求得到了重视.
- 有效和快速的COVID-19诊断对于疾病管理至关重要.
- 人工智能 (AI) 为医学诊断提供了潜在的解决方案.
研究的目的:
- 从胸部X射线图像设计和评估基于AI的分类模型,用于从胸部X射线图像中诊断COVID-19.
- 为了比较浅层卷积神经网络 (CNN) 与更复杂的Xception架构的性能.
- 评估拟议模型的诊断准确性,精度,回忆和F1分数.
主要方法:
- 使用三个卷积块开发一个浅CNN模型.
- 实现和测试基于Xception架构的COVID-19分类模型.
- 使用亚当和随机梯度下降 (SGD) 优化器评估模型性能.
主要成果:
- 浅CNN模型实现了96%的准确性,证明了计算效率.
- 与Adam进行优化的Xception Net模型达到99.94%的卓越精度.
- 该Xception模型实现了100%的精度,回忆和F1得分,表明了最先进的性能.
结论:
- 人工智能模型,特别是Xception架构,在通过胸部X射线诊断COVID-19方面表现出色.
- 开发的模型为疾病检测提供了一个高度准确和高效的工具.
- 这项研究可以支持决策过程,以减少COVID-19死亡率和发病率.
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