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乳腺癌诊断使用虚拟化和基于深度输送前传网络的极端学习算法.

G Siva Shankar1, Edeh Michael Onyema2,3, Balasubramanian Prabhu Kavin4

  • 1Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu, Tamil Nadu, India.

Biomedical engineering and computational biology
|November 4, 2024
PubMed
概括

这项研究介绍了一种基于云的先进机器学习方法,用于早期发现乳腺癌. 这种新的方法实现了高精度,有助于远程诊断,改善了患者的治疗结果.

关键词:
远程诊断 远程诊断 远程诊断改善乌料-ELM 的情况.基于深度残留的多类,用于特征提取方法.智能窗口遗迹删除 智能窗口遗迹删除

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科学领域:

  • 医学成像和诊断 医学成像和诊断
  • 医疗保健中的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 乳腺癌是全球女性的主要死亡原因,需要改进早期检测和治疗策略.
  • 云计算和机器学习为远程诊断和远程医疗提供解决方案,特别是在服务不足的地区.
  • 人工神经网络 (ANN) 显示出疾病诊断的前景,推动了对先进计算方法的研究.

研究的目的:

  • 开发和评估一个新的基于云的机器学习框架,用于准确高效的乳腺癌诊断.
  • 提高早期检测能力,从而减少与乳腺癌相关的死亡率.
  • 利用先进的人工智能技术,提高远程医疗机构的诊断准确性.

主要方法:

  • 采用了四个阶段的方法,包括预处理,特征提取和分类.
  • 智能窗口删除 (SWVD) 技术,结合Savitzky-Golay (S-G) 光滑和自适应过,用于预处理.
  • 基于深度残留的架构多类 (DRMFA) 用于从组织图像中提取特征,然后使用自定义乌料ELM (ACF-ELM) 进行分类.

主要成果:

  • 拟议的基于云的极端学习机器 (ELM) 方法表现出与最先进技术相美的性能.
  • 在DDSM和INbreast数据集上进行评估时,ACF-ELM方法的性能优于其他替代解决方案.
  • 实现了高性能指标:0.9845准确度,0.96精度,0.94回忆和0.95F1得分.

结论:

  • 开发的基于云的机器学习系统为乳腺癌诊断提供了强大而准确的解决方案.
  • 新的SWVD和ACF-ELM技术有助于改善医学成像中的特征提取和分类.
  • 这种方法在增强远程医疗服务和改善乳腺癌结果方面具有重大潜力,特别是在偏远地区.