使用基于计算机断层图像的卷积神经网络的转移学习组合,自动诊断COVID-19患者的阶段
Parisa Gifani1, Majid Vafaeezadeh2, Mahdi Ghorbani3
1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Journal of medical signals and sensors
|July 14, 2023
概括
一个先进的机器学习系统从胸部CT扫描中准确诊断COVID-19的阶段. 这种自动化的深度转移学习组合可以帮助医生做出患者护理决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 胸部计算机断层扫描 (CT) 对于诊断COVID-19阶段,指导住院和患者护理至关重要.
- 专家放射科医生对CT扫描的解释通常是不可用的,主观的和耗时的.
- 需要自动诊断系统来克服专家可用性和解释主观性的局限性.
研究的目的:
- 开发一种先进的机器学习系统,使用肺部CT图像来诊断COVID-19的各个阶段 (正常,早期,渐进,峰值,吸收).
- 使用自动深度转移学习组合来提高诊断准确度.
- 为分类COVID-19疾病进展提供可靠的工具.
主要方法:
- 采用使用预训练卷积神经网络 (CNN) 的深度转移学习策略.
- 胸部CT图像上的微调CNN和带有softmax层的分类提取特征.
- 开发了一种组合方法,将多个深度转移学习模型的多数投票结合起来,以提高性能.
主要成果:
- 一组EfficientNetB4,InceptionResV3和NasNetlarge的组合实现了最高的准确性.
- 拟议的系统在分类COVID-19阶段时显示了91.66%的诊断准确性.
- 实验结果在689个病例的数据集上得到了验证.
结论:
- 拟议的机器学习系统准确地分类COVID-19疾病阶段.
- 这种自动化方法可以帮助医生做出明智的患者护理决策.
- 该系统为主观和专家依赖的CT扫描解释所面临的挑战提供了潜在的解决方案.
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