从计算机断层扫描图像中检测COVID-19使用切片处理技术和修改的Xception分类器
Kenan Morani1, Esra Kaya Ayana2, Dimitrios Kollias3
1Izmir Democracy University Uckuyular, Gursel Aksel Blv No: 14 35140, Karabaglar, Izmir, Türkiye.
International journal of biomedical imaging
|June 17, 2024
概括
这项研究通过使用精益转移学习模型和改进的图像处理来增强CT扫描中的COVID-19检测. 该方法在诊断2019年新冠肺炎疾病时显示出更高的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 准确的COVID-19诊断对于患者管理和公共卫生至关重要.
- 计算机断层扫描 (CT) 图像是检测COVID-19的一个关键工具.
- 现有的诊断模型需要改进,以提高准确性和效率.
研究的目的:
- 开发一种使用CT图像进行COVID-19检测的增强计算方法.
- 通过减少模型错误分类来提高COVID-19诊断的准确性.
- 利用转移学习来高效有效地分析医学成像数据.
主要方法:
- 使用基于Xception架构的精益转移学习模型.
- 图像预处理涉及去除极端切片和手动的肺部区域裁剪.
- CT扫描被调整为224x224的尺寸,并使用修改的Xception模型进行处理.
- 该模型在一个大型,严格注释的CT图像数据库 (>5000名患者) 上进行了训练和验证.
主要成果:
- 与以前的解决方案和基线模型相比,增强方法显示了更好的性能.
- 拟议的方法在COV19-CT数据库上实现了与表现最好的方法相匹配的性能.
- 在未见的图像上的验证证实了模型在测试分区上的有效性.
结论:
- 开发的转移学习模型为通过CT扫描准确检测COVID-19提供了一个有希望的方法.
- 图像处理技术有效地减少了错误分类,并突出了相关的肺部特征.
- 未来的研究应该侧重于可扩展性,适应性和与先进的图像分析技术的集成.
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