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一种支持矢量机器方法用于识别多叶膜溢出
Catur Edi Widodo1, Kusworo Adi1, Rahmad Gernowo1
1Department of Physics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia.
Heliyon
|January 25, 2024
概括
本研究介绍了一种使用支持矢量机器 (SVM) 的机器学习方法,用于在胸部图像中准确检测胸膜溢出. 开发的算法达到96%的准确性,为医学诊断提供了一个有前途的工具.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 胸腔溢血诊断通常依赖于胸部图像的主观解释.
- 需要客观和自动化的方法来提高诊断的准确性和效率.
研究的目的:
- 开发和评估基于机器学习的算法,用于在胸部图像中自动识别胸膜溢出.
- 评估支持矢量机 (SVM) 在将胸部图像归类为正常或含有多叶流液时的性能.
主要方法:
- 使用支持矢量机器 (SVM) 算法来检测膜溢出.
- 图像处理的关键步骤包括感兴趣区域 (ROI) 确定,细分,形态操作,以及度和斜率值的测量.
- 该SVM模型在100张胸部图像 (50张流,50张正常) 上进行了训练,并在50张图像 (25张流,25张正常) 上进行了测试.
主要成果:
- 开发的基于SVM的方法在识别胸腔溢出时实现了96%的高诊断准确度.
- 该算法在基于图像特征的基础上,证明了在区分正常和流病例的有效性.
结论:
- 使用SVM的拟议机器学习方法是一种可靠和准确的方法,用于检测胸部图像中的多叶流液.
- 这种自动化系统有潜力帮助临床医生诊断腹腔液,提高诊断一致性.
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