转移学习用于胸部X射线图像中肺结核的自动两类分类
Akansha Nayyar1, Rahul Shrivastava1, Shruti Jain2
1Department of BT & BI, Jaypee University of Information Technology (JUIT), Solan, H.P., India.
Current medicinal chemistry
|July 23, 2025
概括
使用卷积神经网络 (CNN) 的人工智能 (AI) 可以通过胸部X射线 (CXR) 加快结核病 (TB) 诊断. 发明v3模型实现了高精度,显示了全球结核病检测工作的前景.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 结核病 (TB) 仍然是一个重要的全球卫生问题,由Mycobacterium tuberculosis引起.
- 使用胸部X射线 (CXRs) 早期诊断结核病对于有效治疗至关重要.
- 某些地区的医疗专业知识有限,阻碍了及时和准确的结核病诊断.
研究的目的:
- 应用人工智能 (AI),特别是卷积神经网络 (CNN),用于使用CXR图像快速诊断结核病.
- 开发一种用于结核病检测的自动化系统,特别有利于那些医疗专业知识的获取有限的地区.
- 评估不同CNN架构在从医学图像中识别结核病方面的有效性.
主要方法:
- 使用预先训练的CNN模型:VGG-16,VGG-19,ResNet 50和Inception v3进行CXR图像分析.
- 利用每个模型的独特特征提取能力进行结核病分类.
- 评估模型性能,有或没有图像预处理技术.
主要成果:
- 发明v3模型在结核病检测方面表现出卓越的性能.
- 在没有预处理的情况下达到97.60%的精度,并在预处理后达到98.78%的精度.
- 表明CNN在CXR图像中识别细微的结核病标志性模式的有效性.
结论:
- 这种由人工智能驱动的模型显示了提高结核病诊断的准确性和速度的巨大潜力.
- 这种方法可以帮助减少全球结核病负担.
- 为了更广泛的验证和实施,建议对更大,更多样化的数据集进行进一步的研究.
关键词:
结核病菌菌菌的结核病菌的结核病菌.结核病的诊断 结核病的诊断人工智能的人工智能是人工智能.胸部X射线图像 胸部X射线图像卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.结核病 结核病 结核病 结核病更多相关视频
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