COV-MobNets:基于胸部X射线图像的COVID-19诊断的移动网络合奏模型
Mohammad Amir Eshraghi1, Ahmad Ayatollahi2, Shahriar Baradaran Shokouhi2
1School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. m_eshraghi77@vu.iust.ac.ir.
BMC medical imaging
|June 15, 2023
概括
一个新的COV-MobNets模型通过使用移动设备的X射线准确诊断COVID-19. 这种深度学习框架有助于放射科医生,提高诊断的速度和准确性,特别是在工作量较高的时候.
科学领域:
- 医学成像分析分析 医学成像分析
- 深度学习在医疗保健中的应用.
- 移动卫生技术 移动卫生技术
背景情况:
- 医疗行业的过度工作量需要先进的诊断工具.
- 计算机辅助诊断 (CAD) 和移动辅助诊断 (MAD) 系统可以提高诊断速度和准确性.
- 流行病状况凸显了需要远程和高效的诊断解决方案的需要.
研究的目的:
- 从胸部X射线开发一个移动友好的深度学习框架,用于从胸部X射线预测COVID-19.
- 创建可在便携式设备上部署的工具,以帮助放射科医生,特别是在工作量较高的时期.
- 提高 COVID-19 人口查的准确性和透明度.
主要方法:
- 提出了COV-MobNets组合模型,将MobileViT (变压器) 和MobileNetV3 (CNN) 结合起来.
- 采用双特征提取方法,用于增强胸部X射线图像分析.
- 利用数据增强技术和COVIDx-CXR-3数据集进行培训和验证.
主要成果:
- COV-MobNets的分类准确率达到97.75%,超过了单个模型的表现.
- 该模型表现出高灵敏度 (98.5%) 和特异性 (97%).
- 实验性比较证实,与其他方法相比,其精度和平衡性更高.
结论:
- 拟议的框架有效地通过X射线诊断COVID-19以更高的准确性和速度.
- 结合多种特征提取器可以提高诊断性能和概括性.
- COV-MobNets为计算机辅助和移动辅助的COVID-19诊断提供了一个可行的解决方案,可提供开放访问代码.
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