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相关实验视频

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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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一种高效的脑瘤细分方法,基于自适应移动自我组织地图和模糊的K-Mean集群.

Surjeet Dalal1, Umesh Kumar Lilhore2, Poongodi Manoharan3

  • 1Department of Computer Science and Engineering, Amity University Gurugram, Gurugram 122412, Haryana, India.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
概括

这项研究介绍了一种高效的自适应移动自我组织地图和模糊k-means集群 (AMSOM-FKM) 方法,用于MRI扫描中的脑瘤细分. 与现有方法相比,AMSOM-FKM技术显著提高了瘤检测的准确性.

关键词:
K-意味着K的意思是K.适应性自我组织地图大脑瘤是个大脑瘤灰色层次 co 灰色层次共发生矩阵.医学成像医学成像

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相关实验视频

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算神经科学是一种神经科学.

背景情况:

  • 在磁共振成像 (MRI) 中的大脑瘤细分是一个复杂的挑战.
  • 机器学习在改善瘤检测和细分精度方面表现有前途.
  • 现有的方法往往难以准确地划定瘤边界.

研究的目的:

  • 利用MRI数据开发一种高效的脑瘤检测和细分技术.
  • 为了提高瘤区域提取的准确性和效率.
  • 评估拟议方法的性能与既有技术相比.

主要方法:

  • 使用了Kaggle Brats-18脑瘤数据集 (1691张图像).
  • 雇员自适应移动自组织地图 (AMSOM) 用于无监督的特征学习和分类.
  • 应用 模糊k-平均值 (FKM) 聚类用于精确的瘤区域细分.
  • 集成的Wiener过器用于消除噪音和灰色水平共发生矩阵 (GLCM) 用于特征提取.

主要成果:

  • 拟议的AMSOM-FKM技术在脑瘤细分方面表现出卓越的性能.
  • 与模糊C-平均和K-平均方法相比,在准确度,灵敏度,精度和相似度指数方面取得了超过10%的改进.
  • 成功细分瘤区域,将其与周围组织区分开来.

结论:

  • AMSOM-FKM方法为MRI中脑瘤细分提供了一种高效和准确的方法.
  • 这种技术有可能在神经瘤学中获得临床应用.
  • 进一步的研究可以探索其在各种脑瘤数据集上的应用.