Search research articles

相关概念视频

Classification of Neurotransmitters

Classification of Neurotransmitters

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...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

An advanced ensemble deep learning framework for accurate multi-class lung cancer classification using IUNet++ and MResNext.

Computational biology and chemistry·2026

Same author

An intelligent federated learning boosted cyberattack detection system for Denial-Of-Wallet attack using advanced heuristic search with multimodal approaches.

Scientific reports·2025

查看所有相关文章

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Jan 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

使用深度学习的增强脑瘤分类框架.

Ramesh Babu Vure¹, Lalitha Kumari Pappala²

¹VIT-AP University, Amaravati, Andhra Pradesh, India.

Scientific reports

|October 14, 2025

概括

此摘要是机器生成的。

这项研究引入了使用生成对抗网络 (GAN) 和DMFN进行改进脑瘤分类的先进深度学习框架,在BRATS2021数据集上实现了98.36%的准确性.

关键词:

脑瘤分类大脑瘤的分类深度学习是一种深度学习.没有了,没有了,没有了.这就是PCA-PSO.在ResNet18中使用ResNet18

更多相关视频

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

相关实验视频

Last Updated: Jan 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

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:

脑瘤诊断需要准确的工具,用于早期检测和分类.
传统的方法在早期瘤检测和分类方面扎.
深度学习模型面临的挑战是复杂的数据集和有限的标记数据.

研究的目的:

开发一个先进的深度学习框架,用于准确的脑瘤分类.
为了提高对质瘤,脑膜瘤,无瘤和垂体瘤的分类准确性.
通过数据增强技术来解决数据限制.

主要方法:

利用生成对抗网络 (GANs) 来进行数据增强.
使用ResNet18来有效地从医疗图像中提取特征.
开发了一个深度多视图融合网络 (DMFN) 模型,使用多个ResNet18实例进行分类.
集成的PCA-PSO用于功能选择.

主要成果:

在BRATS2021数据集上实现了98.36%的验证准确性.
培训损失减少到0.1963和验证损失减少到0.1382.
在大脑瘤分类方面,与现有技术相比显著改进.

结论:

拟议的框架显示了推进脑瘤诊断的巨大潜力.
GAN,PCA-PSO和DMFN的组合为医疗图像分析提供了一个强大的方法.
这个框架可以应用于其他医学成像任务,以改善临床结果.