使用深度学习技术分析胸部X射线图像以检测COVID-19感染的全面审查
Kavitha Subramaniam1, Natesan Palanisamy1, Renugadevi Ammapalayam Sinnaswamy2
1Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India.
概括
人工智能 (AI) 和深度学习,特别是卷积神经网络 (CNN),显示出使用胸部X射线自动检测COVID-19的前景. 本综述评估了人工智能方法,以实现更快,更准确的诊断,减少手动错误.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- COVID-19诊断依赖于胸部X射线 (CXRs),但手动解释是耗时且容易出错的.
- 人工智能 (AI) 提供了来自CXRs的自动和准确COVID-19检测的潜力.
- 卷积神经网络 (CNN) 是用于图像分类任务的关键深度学习技术.
研究的目的:
- 通过使用CXR图像审查和批判性评估现有关于人工智能和深度学习用于COVID-19检测的研究.
- 分析用于COVID-19诊断的深度学习架构中的方法,算法,预处理技术和数据集.
- 为了确定人工智能驱动的COVID-19成像分类的未来研究方向.
主要方法:
- 对人工智能/深度学习用于COVID-19CXR诊断的预印件和已发表的研究进行系统审查.
- 分析各种深度学习架构,包括CNN.
- 评估预处理技术,数据集和性能指标.
主要成果:
- 深度学习,特别是CNN,显示出来自CXRs自动检测COVID-19的巨大潜力.
- 不同的人工智能方法在提高诊断速度和准确性方面表现有前途.
- 性能根据数据集,算法和预处理方法而有所不同.
结论:
- 人工智能和深度学习是通过CXR分析增强COVID-19诊断的宝贵工具.
- 需要进一步的研究来优化算法和标准化临床应用的方法.
- 未来的方向包括探索新的架构和改善跨不同数据集的概括.
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