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通过随机森林分类器在胸部X射线中检测Covid-19使用深度CNN和DWT优化功能的混合

Rafid Mostafiz^1,2, Mohammad Shorif Uddin³, Nur-A- Alam^1,2

¹Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh.

Journal of King Saud University. Computer and information sciences

|April 15, 2024

概括

这项研究引入了一种人工智能方法,用于使用深度学习和波形特征从胸部X射线检测COVID-19. 该方法的准确度超过98.5%,为快速诊断疾病提供了一个有前途的工具.

科学领域:

医疗成像医学成像
人工智能的人工智能
信号处理信号处理

背景情况:

胸部X射线对于诊断各种疾病至关重要.
准确和早期发现COVID-19对于患者管理和公共卫生至关重要.
现有的诊断方法可能耗时或需要专门的设备.

研究的目的:

利用胸部X射线图像开发用于COVID-19检测的智能系统.
为了利用深层卷积神经网络 (CNN) 和离散波量变换 (DWT) 的力量来增强特征提取.
通过自动化方法提高COVID-19诊断的准确性和效率.

主要方法:

胸部X射线图像的预处理,包括增强和细分.
混合功能提取使用深度CNN和DWT.
使用最小冗余最大相关性 (mRMR) 和递归特征消除 (RFE) 的特征优化.
使用基于森林的随机包装组合方法进行分类.

主要成果:

拟议的混合方法实现了超过98.5%的高检测精度.
结合CNN和DWT功能,并通过mRMR和RFE进行优化,显著提高了诊断性能.
随机森林分类器有效地区分了COVID-19病例和其他条件.

结论:

开发的智能系统在通过胸部X射线检测COVID-19方面表现出高效.
这种人工智能驱动的方法为传统诊断技术提供了可靠和准确的替代方案或补充.
这种方法显示出在临床环境中广泛应用的潜力,以帮助医学专家诊断疾病.

关键词:

卷积神经网络 (CNN) 是一种神经网络.这就是Covid-19的原因.离散波形变换 (DWT) 是指离散波形变换.最小冗余的最大相关性 (mRMR)随机森林分类器是随机森林分类器.递归特征消除 (RFE) 是一种方法.

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