使用计算机视觉技术,自动检测胸部X射线中的异常
1Department of Computer Information Systems, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt 19117, Jordan.
Diagnostics (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种先进的机器学习算法,用于检测X射线中的肺异常,提高结核病和肺结节等疾病的诊断准确度.
科学领域:
- 医疗成像医学成像
- 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:
- 从胸部X射线诊断肺部疾病的准确和有效诊断对于有效的患者治疗至关重要.
- 传统的诊断方法有时会忽略微妙的异常,需要先进的分析工具.
研究的目的:
- 开发和验证一种先进的机器学习算法,用于准确检测胸部X射线图像中的异常.
- 为医疗保健专业人员提供可靠的工具,用于诊断各种肺部疾病,包括呼吸道感染,结核病 (TB) 和肺结节.
主要方法:
- 利用大量的胸部X射线图像进行分析.
- 采用深度学习 (DL) 算法,对象识别和分类模型,特别适应了You Only Look Once (YOLO) v8算法.
- 实施数据增强技术,如缩放,旋转和模仿,以增强训练数据集的多样性.
主要成果:
- 经过修改的YOLO v8算法有效地将X射线图像分为不同类别的肺部异常.
- 该算法在识别常常被传统诊断方法遗漏的独特和关键发现方面表现得非常准确.
- 取得了可靠的异常检测,这些异常标志着呼吸道感染,结核病 (TB) 和肺结节.
结论:
- 机器学习 (ML) 算法,特别是像YOLO v8这样的高级DL模型,为诊断肺部疾病提供了可靠和高效的工具.
- 开发的算法增强了诊断能力,使医疗保健从业人员能够在识别肺部疾病时实现更高的准确性和效率.
关键词:
在这里,我们可以看到CAD,CAD,CAD.这些异常是异常.计算机视觉技术的使用深度学习算法深度学习算法图像的分类图像的分类.图像处理是图像处理的过程.图像技术 图像技术 图像技术机器学习是机器学习.磁共振成像技术的使用对象检测检测对象检测对象检测肺炎是一种肺炎.更多相关视频
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