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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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相关实验视频

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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进化引力新认知子神经网络优化与海洋捕食者优化算法MRI脑瘤分类的优化算法.

A Lakshmi1, Manjunathan Alagarsamy2, A Anbarasa Pandian3

  • 1Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India.

Electromagnetic biology and medicine
|January 13, 2024
PubMed
概括

这项研究引入了一种使用磁共振成像 (MRI) 进行脑瘤分类的新方法. 拟议的进化引力新认知子神经网络与海洋捕食者算法 (EGNNN-MPA) 显著提高了检测脑瘤的准确性.

关键词:
进化引力新认知子神经网络 (EGNNN)海洋捕食者算法 (MPA) 是一个算法.萨维茨基 - 戈莱脱氧化方法视觉几何组网络 (VGG16) 是一个视觉几何组网络.

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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:

  • 磁共振成像 (MRI) 对于人类大脑瘤诊断至关重要.
  • 现有的脑瘤检测方法往往缺乏准确性和效率.
  • 准确分类瘤类型 (脑膜瘤,质瘤,垂体瘤) 和正常组织是具有挑战性的.

研究的目的:

  • 提出磁共振成像 (MRI) 脑瘤分类 (BTC) 的先进方法.
  • 为了提高脑瘤检测的准确性和减少计算时间.
  • 介绍进化引力新认知子神经网络,该神经网络与海洋捕食者算法 (EGNNN-MPA) 进行了优化,用于MRI-BTC.

主要方法:

  • 利用Brats的MRI图像数据集用于脑瘤图像.
  • 使用Savitzky-Golay Denoising方法进行预处理的图像.
  • 使用视觉几何组网络 (VGG16) 提取的特征 (灰色水平,哈拉利克纹理).
  • 使用EGNNN分类器与VGG16集成,并使用海洋捕食者优化算法 (MPA) 优化重量.

主要成果:

  • 该EGNNN-VGG16-MPA-MRI-BTC方法显示出卓越的性能.
  • 与现有模型 (AlexNet-SVM,RESNET-SGD,MobileNet-V2) 相比,在准确性,精度和灵敏度方面取得了显著的改进.
  • 与比较方法相比,发现了38.98%,46.74%,23.27%的特定准确度增长.

结论:

  • 拟议的EGNNN-VGG16-MPA-MRI-BTC模型为MRI扫描的脑瘤分类提供了一个高度准确和高效的解决方案.
  • 这种方法解决了先前方法在准确性和计算成本方面的局限性.
  • 该研究强调了将先进的神经网络和优化算法集成到医疗图像分析中的潜力.