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相关概念视频

Brain Imaging01:14

Brain Imaging

203
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
203

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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对大脑瘤分类和检测的上下文感知机器学习技术 - - 综述

Usman Amjad1, Asif Raza2, Muhammad Fahad3

  • 1NED University of Engineering and Technology, Karachi, Pakistan.

Heliyon
|February 5, 2025
PubMed
概括

机器学习,特别是卷积神经网络 (CNN),通过MRI和组织病理学来增强脑瘤的分类和细分. 这些人工智能方法对准确的诊断和改进的治疗规划有希望.

关键词:
在美国,CNN是CNN.深度学习是一种深度学习.历史学 历史学 历史学在 K-MEANS 集群化中.这就是为什么MRI是MRI.机器学习 机器学习预测生存的预测.瘤细分 瘤的细分

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科学领域:

  • 在瘤学中使用人工智能
  • 医学图像分析 医学图像分析
  • 计算病理学计算病理学

背景情况:

  • 机器学习 (ML) 在急性医疗护理中为确切诊断,预测和分类脑瘤提供了巨大的潜力.
  • 恶性质瘤是侵袭性脑瘤;了解它们的遗传异常有助于分类和预后.
  • 最近对脑瘤的遗传洞察力支持改善了他的病理学和生物特征.

研究的目的:

  • 使用ML技术审查和预测基因变异,将它们与各种瘤类型相关联.
  • 通过先进的ML探索基因突变和结构的预测,专注于多模态MRI和组织病理学数据.
  • 利用卷积神经网络 (CNN) 在脑瘤分类中的图像处理和分析.

主要方法:

  • 对MRI和组织学图像处理用于瘤分类的最新进展的综述 (质瘤,脑膜瘤,垂体瘤,小质质瘤,星细胞瘤).
  • 应用各种神经网络架构来应对瘤分类,细分,数据集和模式方面的挑战.
  • 对CNN表现的竞争性分析以及K-MEANS聚类对预测遗传结构和分子变化的影响.

主要成果:

  • 通过提取基于图像的特征,CNN和K-Nearest Neighbors (KNN) 有效地对瘤进行分类和细分,克服了图像分析的挑战.
  • 在公共数据集上,CNN算法在瘤分类和细分方面表现优于其他方法.
  • 该研究强调了CNN在精确瘤诊断和治疗规划方面的潜力.

结论:

  • ML,特别是CNN和支持矢量机 (SVM) 算法,显示出使用成像和组织病理学进行准确的大脑瘤诊断和分类的巨大潜力.
  • 预计ML的进步将提高手术内瘤诊断的准确性和效率.
  • 对神经瘤学ML的进一步研究可以提高治疗策略和患者的治疗结果.