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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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使用计算机辅助诊断系统的白血病检测和分类,使用猎优化算法和深度学习的计算机辅助诊断系统.

Turky Omar Asar1, Mahmoud Ragab2

  • 1Department of Biology, College of Science and Arts at Alkamil, University of Jeddah, Jeddah, Saudi Arabia.

Scientific reports
|September 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于白血病检测的新深度学习方法,达到99.62%的准确性. 通过精确分类白细胞,FOADCNN-LDC技术提高了早期癌症诊断.

关键词:
生物启发的算法癌症的诊断 癌症的诊断计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.图像处理 图像处理医学成像医学成像

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科学领域:

  • 医学诊断 医学诊断 医学诊断
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 白血病是一种血液癌症,涉及骨髓中异常白细胞 (WBC) 的生长.
  • 早期和准确的白血病检测是具有挑战性的,因为当前的诊断工具,如流细胞计的局限性.
  • 现有的计算机辅助诊断 (CAD) 和机器学习 (ML) 方法旨在改善白血病分析.

研究的目的:

  • 提出一种新的深度学习技术,用于准确的白血病检测和分类.
  • 解决耗时且不太准确的传统诊断方法的局限性.
  • 通过先进的计算方法提高白血病的早期诊断.

主要方法:

  • 开发了一个深度卷积神经网络,与猎优化算法 (FOADCNN-LDC) 集成.
  • 中位过 (MF) 用于医疗图像的初始降噪.
  • 使用ShuffleNetv2进行高效的特征提取,然后使用卷积无序自编码器 (CDAE)进行分类.
  • 猎优化算法 (FOA) 优化了CDAE模型的超参数.

主要成果:

  • FOADCNN-LDC技术在白血病检测和分类方面表现出高性能.
  • 拟议的方法在基准医疗数据集上实现了99.62%的卓越准确性.
  • 对比分析表明,FOADCNN-LDC在准确性方面优于现有技术.

结论:

  • FOADCNN-LDC技术为白血病诊断提供了一个高度准确和高效的方法.
  • 这种深度学习模型显示了改善临床环境中早期癌症检测的巨大潜力.
  • 该研究强调了将优化算法与医学图像分析的深度学习相结合的有效性.