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学习模型的绩效评估用于COVID-19的预后
Baijnath Kaushik1, Akshma Chadha1, Reya Sharma1
1School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, India.
一种新的混合深度传输学习技术有效地从胸部X射线图像中检测到COVID-19. 这种方法超越了现有的深度学习和转移学习方法,用于准确检测COVID-19.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 全球COVID-19大流行需要快速准确的检测方法.
- 深度学习和转移学习显示出使用医学图像检测COVID-19的前景.
- 之前的研究经常使用有限的数据集来检测COVID-19.
研究的目的:
- 提出和评估一种用于COVID-19检测的新型混合深度转移学习技术.
- 解决之前的COVID-19检测研究中小型数据集的局限性.
- 将拟议方法的性能与现有的深度学习和转移学习技术进行比较.
主要方法:
- 开发一种混合深度转移学习模型.
- 使用了28,384张胸部X射线图像 (14,192张COVID-19,14,192张正常) 的平衡数据集.
- 对混合技术在胸部X射线数据集上的有效性的实验评估.
主要成果:
- 拟议的混合深度转移学习技术表现出卓越的性能.
- 与当代转移学习和深度学习方法相比,取得了更好的检测结果.
- 在一个大,平衡的胸部X射线数据集上验证该技术的有效性.
结论:
- 混合深度传输学习方法为从X射线图像中检测COVID-19提供了有效的解决方案.
- 拟议的方法比现有技术提供了更好的准确性.
- 突出了先进的人工智能方法在打击传染病流行病中的潜力.
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