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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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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
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Brain Imaging01:14

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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...
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Imaging Studies IV: Magnetic Resonance Imaging01:27

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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相关实验视频

Updated: Jul 15, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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从MRI使用图像增强和卷积神经网络技术进行脑瘤分类.

Zahid Rasheed1, Yong-Kui Ma1, Inam Ullah2

  • 1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.

Brain sciences
|September 28, 2023
PubMed
概括

这项研究引入了一种先进的深度学习模型,用于使用增强的MRI图像进行准确的脑瘤分类. 这种新方法的准确率超过97%,帮助医生进行精确的诊断.

关键词:
大脑瘤是个大脑瘤这是分类分类的分类.深度学习是一种深度学习.医疗保健 医疗保健 医疗保健 医疗保健磁共振成像技术的使用神经网络的神经网络的神经网络预先训练有素的模型.

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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
  • 在瘤学瘤学.

背景情况:

  • 通过MRI检测大脑瘤是复杂的,容易出现错误.
  • 深度学习 (DL) 为医疗图像分析提供了自动化解决方案.
  • 卷积神经网络 (CNN) 在图像分类任务中是有效的.

研究的目的:

  • 开发和验证一种新的深度学习方法来分类脑瘤 (质瘤,脑膜瘤,垂体瘤) 和MRI的非瘤病例.
  • 整合图像增强技术,以提高分类性能.
  • 将拟议的模型与已建立的预训练模型进行比较.

主要方法:

  • 实现了一种结合高斯模糊利和CLAHE自适应式直方图等级的新型模型,以增强图像.
  • 使用深度学习方法对脑瘤进行分类.
  • 使用基准数据集进行验证,并与VGG16,ResNet50,VGG19,InceptionV3和MobileNetV2.2进行比较.

主要成果:

  • 拟议的方法实现了97.84%的分类精度.
  • 精度,回忆和F1得分超过97.85%,表明性能高.
  • 该模型在不同类型的瘤中展示了强大的概括能力.

结论:

  • 开发的方法精确地分类了常见的大脑瘤类型,具有高精度.
  • 该技术显示出作为医生在脑瘤诊断中的宝贵工具的巨大潜力.
  • 将图像增强与DL集成,可以提高诊断准确性和效率.