通过硬投票组合方法进行基于深度学习的COVID-19检测
Asaad Qasim Shareef1, Sefer Kurnaz1
1Department of Electrical Computer Engineering, Altinbas University, Istanbul, Turkey.
概括
这项研究介绍了使用X射线图像检测COVID-19的集体深度学习模型. 该方法实现了高准确性,有助于早期诊断,减少医疗保健系统的压力.
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 诊断 传染病诊断
背景情况:
- 全球医疗保健系统因COVID-19大流行而面临巨大的压力.
- 早期和准确的诊断对于控制病毒传播和治疗患者至关重要.
- 医学成像,特别是X射线,为肺部疾病提供了宝贵的见解.
研究的目的:
- 开发和评估使用X射线图像进行COVID-19识别的新型整体方法.
- 为了提高诊断性能,利用深度学习和转移学习.
- 在资源有限的环境中提高COVID-19诊断的效率.
主要方法:
- 一种组合方法,将CNN,VGG16和DenseNet模型的信任分数结合起来,使用硬投票.
- 转移学习的应用,以优化有限的医疗图像数据集的性能.
- 使用X射线PIC (X射线图片) 进行COVID-19检测.
主要成果:
- 拟议的组合方法实现了97%的准确性,96%的精度,100%的回忆和98%的F1分数.
- 与现有的诊断技术相比,其表现优越.
- 验证了转移学习在提高模型性能方面的有效性.
结论:
- 合并方法与转移学习相结合,显示出通过X射线进行COVID-19诊断的显著前景.
- 在X射线-PIC方法可以大大帮助早期疾病检测.
- 这种方法有可能减轻全球医疗保健系统的负担.
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