CovMediScanX:一种医学成像解决方案,用于从胸部X射线图像中诊断COVID-19
Smitha Sunil Kumaran Nair1, Leena R David2, Abdulwahid Shariff3
1Department of Computing and Electronics Engineering, Middle East College, Sultanate of Oman.
Journal of medical imaging and radiation sciences
|April 9, 2024
概括
CovMediScanX在胸部X射线上使用深度学习来快速诊断COVID-19. 定制的CNN实现了94.32%的准确性,显示了自动查的前景,尽管对于正常病例检测需要进一步改进.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 放射科医生使用胸部X射线 (CXR) 来识别COVID-19的视觉标记物.
- 介绍了一个深度学习框架CovMediScanX,用于自动化CXR图像的COVID-19诊断.
研究的目的:
- 开发和评估用于使用CXR扫描快速和自动检测COVID-19的深度学习框架.
- 从CXR图像中确定COVID-19分类的最佳深度学习模型.
主要方法:
- 收集和预处理CXR图像数据集.
- 培训定制的卷积神经网络 (CNN),预训练和混合转移学习模型.
- 将表现最好的模型嵌入CovMediScanX网络界面,供放射科医生使用.
主要成果:
- 一个定制的CNN模型实现了94.32%的测试准确性,超过了其他模型.
- 在一个独立的数据集上,CovMediScanX显示了73%的精度和84%的回忆COVID-19阳性病例.
- 该框架需要加强,以便更好地检测正常病例.
结论:
- 该CovMediScanX框架显示了从CXR自动识别COVID-19的潜力.
- 通过使用多样化的培训数据来解决模型偏差,可以进一步提高准确性和可靠性.
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