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基于高性能计算的脑瘤检测使用并行量子扩展卷积神经网络.

Sushama Seetaram Shinde1, Aparna Pande2

  • 1Department of Computer Science and Engineering, SunRise University, Alwar, Rajasthan, India.

NMR in biomedicine
|April 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的并行量子扩展卷积神经网络 (PQDCNN),用于使用大数据准确检测脑瘤. 通过克服现有的磁共振成像方法的局限性,PQDCNN模型显著改善了早期诊断.

关键词:
梅达夫过器可以过.在TransBTSV2中模糊的局部信息 C-表示集群.地图减速器地图减速器平行卷积神经网络是一种并行卷积神经网络.量子扩展卷积神经网络的神经网络.

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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:

  • 大脑瘤的特点是细胞生长异常,需要精确的检测方法.
  • 磁共振成像 (MRI) 对于识别脑瘤至关重要,但目前的技术面临着计算复杂性,噪音和准确性的挑战.
  • 早期和精确的脑瘤诊断对于有效的治疗和患者的治疗结果至关重要.

研究的目的:

  • 提出一种新的,高性能计算方法,用于增强脑瘤检测.
  • 解决现有的MRI分析方法的局限性,包括噪声干扰和低精度.
  • 开发和验证一个并行量子扩展卷积神经网络 (PQDCNN) 以改善脑瘤识别.

主要方法:

  • 一个基于大数据的检测模型,使用具有Map-Reducer架构的并行量子扩展卷积神经网络 (PQDCNN).
  • 使用模糊局部信息C-Means集群 (FLICM) 执行的数据分区.
  • 通过Medav过去除噪音,使用TransBTSV2变压器模型进行瘤细分,然后在Map-Reducer框架内进行图像增强和特征提取.

主要成果:

  • 拟议的PQDCNN模型在脑瘤检测方面取得了高性能.
  • 验证指标显示出卓越的效率:91.52%的准确性,91.69%的灵敏性和92.26%的特异性.
  • 这种新的方法有效地克服了传统方法中普遍存在的计算复杂性和噪音问题.

结论:

  • 开发的PQDCNN模型在脑瘤检测准确性和效率方面取得了重大进展.
  • 这种大数据驱动的方法提高了早期诊断能力,有可能改善患者的预后.
  • 集成先进的人工智能技术,如PQDCNN和变压器模型显示了医疗成像分析的巨大前景.