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使用计算智能和集体学习进行高效的肺癌检测.

Richa Jain1, Parminder Singh1, Mohamed Abdelkader2

  • 1School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.

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PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种使用计算智能和医疗物联网 (IoMT) 的AI驱动的肺癌检测模型. 这种创新方法实现了98.50%的准确性,改善了早期诊断和患者护理.

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科学领域:

  • 计算智能是一种计算智能.
  • 医疗信息学医学信息学
  • 医疗保健中的人工智能

背景情况:

  • 肺癌是癌症死亡的主要原因,需要早期检测才能有效治疗.
  • 当前的诊断方法面临着高错误阳性率和精度限制的挑战.
  • 计算智能和医疗物联网 (IoMT) 的进步为改善肺癌检测提供了潜力.

研究的目的:

  • 使用计算智能开发一个生态考虑的肺癌治疗原型模型.
  • 为以患者为中心的肺癌护理提出基于医疗物联网 (IoMT) 的框架.
  • 为了提高肺癌检测的准确性和效率,解决当前的诊断挑战.

主要方法:

  • 利用后勤回归,MLP分类器和高斯 NB分类器来检测肺癌.
  • 采用K-Means和模糊逻辑来进行智能特征选择.
  • 通过投票分类器和使用网格搜索优化的超参数集成集体学习.

主要成果:

  • 在肺癌检测中达到98.50%的高准确率.
  • 与现有的NB,J48和SVM方法相比,表现出优越的性能.
  • 显示了在诊断程序中节省时间和成本的巨大潜力.

结论:

  • 计算智能和IoMT集成可以导致有效和资源高效的肺癌疗法.
  • 拟议的模型在早期和准确的肺癌诊断方面提供了有前途的进展.
  • 这项研究强调了人工智能在改善癌症治疗结果方面的变革潜力.