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相关概念视频

Classification of Neurotransmitters01:30

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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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相关实验视频

Updated: Jul 14, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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基于多模式混合卷积神经网络的脑瘤等级分类.

A Rohini1, Carol Praveen2, Sandeep Kumar Mathivanan3

  • 1Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, 531162, India.

BMC bioinformatics
|October 10, 2023
PubMed
概括

这项研究引入了一种使用VGG-19的深度学习模型,用于准确检测脑瘤. 改进的方法实现了高精度,为传统诊断程序提供了更快,更可靠的替代方案.

关键词:
定制的CNN定制的CNN深度学习是一种深度学习.磁共振图像图像的使用方法转移学习转移学习瘤的分类 瘤的分类在VGG19中,VGG19在VGG19中.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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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:

  • 大脑瘤是异常的细胞生长,可以是癌症或良性,造成严重的健康风险.
  • 目前的诊断方法如CT和MRI是劳动密集型的,并且可能不准确.
  • 需要有高效,准确和成本效益的脑瘤诊断工具.

研究的目的:

  • 开发一种自动化的深度学习模型,用于准确识别脑瘤.
  • 为了提高诊断性能,利用转移学习和卷积神经网络.
  • 为传统的大脑瘤检测方法提供更快,更可靠的替代方案.

主要方法:

  • 使用预先训练的VGG-19模型利用转移学习.
  • 实现了一个定制的卷积神经网络 (CNN) 框架.
  • 应用的预处理技术:规范化和数据增强.
  • 在407张CT图像 (257张瘤图像,150张非瘤图像) 的数据集上训练和测试模型.

主要成果:

  • 取得了99.43%的惊人的准确率.
  • 显示高灵敏度为98.73%,特异性为97.21%.
  • 与传统的诊断方法相比,该模型显示出更高的性能.

结论:

  • 拟议的深度学习模型为大脑瘤检测提供了一个高度准确和高效的解决方案.
  • 这种由人工智能驱动的方法可以显著帮助从CT图像中早期和可靠地诊断脑瘤.
  • 这些发现表明,在开发先进的诊断工具方面,有临床应用的潜力.