基于深度学习的对象检测策略,用于在胸部X射线图像中检测和定位疾病
Yi-Ching Cheng1, Yi-Chieh Hung1, Guan-Hua Huang1
1Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan.
Diagnostics (Basel, Switzerland)
|December 17, 2024
概括
深度学习通过自动检测异常区域来改善胸部X射线分析. 卷积神经网络 (CNN) 的性能优于变压器,提高了呼吸道和心血管疾病的诊断准确性和效率.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 胸部X射线 (CXR) 解释对于诊断呼吸道和心血管疾病至关重要.
- 手动CXR分析面临的挑战包括主观性,时间消耗和诊断错误.
- 需要自动化方法来提高CXR解释的准确性和一致性.
研究的目的:
- 开发和评估基于深度学习的对象检测方法,用于自动识别和注释CXR图像中的异常.
- 评估背景图像比例对模型性能的影响.
- 为了比较卷积神经网络 (CNN) 和基于变压器的模型用于医疗图像分析.
主要方法:
- 利用来自E-Da医院的疾病标记的CXR图像与边界框进行模型开发和测试.
- 调查了各种训练数据集和方法来管理正常图像的流行.
- 探索了一些射击物体检测技术,以解决特定疾病的有限数据.
- 对比了CNN和变压器架构的性能.
主要成果:
- 背景图像的比例显著影响了模型推理.
- 整合二进制分类方案始终提高了模型性能.
- 基于CNN的模型在所有测试的场景中,与基于变压器的模型相比,表现优越.
结论:
- 开发了一种更有效,更可靠的系统,用于自动检测CXR图像中的疾病标签和界限框.
- 深度学习,特别是CNN,为增强CXR分析提供了一个有前途的方法.
- 该研究提供了对优化医疗图像分析的深度学习模型的见解,考虑数据不平衡和架构选择.
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