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基于DCNN和图像处理的胸部X射线的对比研究,用于识别COVID-19患者,并使用微调

Amitesh Badkul¹, Inturi Vamsi², Radhika Sudha¹

¹Department of Electrical and Electronics, Birla Institute of Technology and Science-Pilani, Hyderabad, India.

Journal of medical engineering & technology

|December 9, 2024

概括

这项研究引入了深度卷积神经网络 (DCNN) 模型,用于使用胸部X射线 (CXR) 图像自动检测COVID-19. 图像预处理提高了分类准确性,VGG16显示出卓越的性能.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

使用CT扫描的传统COVID-19检测是耗时的,容易变化.
准确和高效的诊断工具对于管理大流行和其他呼吸道疾病至关重要.

研究的目的:

开发一种自动化系统,从胸部X射线 (CXR) 图像中检测和分类COVID-19.
为了评估深层卷积神经网络 (DCNN) 模型的有效性,并没有图像预处理用于多类分类.

主要方法:

利用了四个类别的胸部X射线 (CXR) 图像:健康,COVID-19,细菌性肺炎和病毒性肺炎.
应用了数据增强和两个预处理技术:对比限度自适应组图平衡 (CLAHE) 和黑帽形态操作 (BMO).
在原始和预处理数据集上训练和评估多个DCNN模型 (DenseNet,MobileNet,ResNet,VGG16,Xception),以进行多类分类.

主要成果:

图像预处理显著提高了DCNN模型的分类准确性,并减少了DCNN模型中的错误.
VGG16模型在多类分类中显示了最高的分类准确度和最低的错误率.
预处理提高了DCNN模型在区分健康,COVID-19和肺炎病例方面的性能.

结论:

使用DCNNs对CXR图像的自动分析为传统诊断方法提供了一个有希望的替代方案.
像CLAHE和BMO这样的图像预处理技术有效地提高了DCNN模型用于呼吸系统疾病分类的性能.
通过预处理增强的VGG16模型显示了临床决策支持的潜力,减少了医疗保健专业人员的工作量.

关键词:

在 COVID-19 疫情中,深度学习是一种深度学习.精细调整精细调整精细调整图像处理是图像处理的过程.波形变换波形变换波形变换.

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