使用深度CNN和StyleGAN2进行COVID-19横流测试图像分类.
Vishnu Pannipulath Venugopal1, Lakshmi Babu Saheer1, Mahdi Maktabdar Oghaz1
1School of Computing and Information Science, Anglia Ruskin University, Cambridge, United Kingdom.
Frontiers in artificial intelligence
|February 13, 2024
概括
一个深度卷积神经网络 (CNN) 模型自动化了COVID-19 RATD图像分类. 这种人工智能方法显示出大规模测试和疫情缓解的潜力,尽管存在数据集挑战.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 人工智能 (AI) 具有改善医疗保健工作流程和诊断准确性的潜力,特别是在 COVID-19 测试等大规模公共卫生计划中.
- 自动图像分类对于有效和准确地解释诊断测试至关重要.
研究的目的:
- 开发和评估一个深度卷积神经网络 (CNN) 模型,用于COVID-19 RATD图像的自动分类.
- 通过数据增强和合成数据生成,解决可用的RATD图像数据集的局限性.
主要方法:
- 一个由900个现实世界COVID-19 RATD图像组成的数据集是众包的.
- 数据增强技术和StyleGAN2-ADA被用来生成合成图像,减轻数据集限制和类不平衡.
- 一个深度的CNN模型在真实和合成数据集上进行了训练和验证.
主要成果:
- 性能最好的CNN模型实现了93%的验证准确性.
- 在测试数据集中,该模型在模拟图像中达到88%的准确性,在真实图像中达到82%的准确性.
- 虽然数据增强提高了模拟图像的性能,但它并没有显著提高现实世界的测试数据的准确性.
结论:
- 开发的AI模型显示了加快COVID-19测试和支持大规模测试和跟踪系统的巨大潜力.
- 解决数据集的局限性和类不平衡对于AI诊断工具的成功开发至关重要.
- 这项研究为应用人工智能来缓解未来传染病爆发提供了宝贵的见解.
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