Search research articles

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

相关概念视频

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

A hybrid improved binary GWO-PSO with random forest (IBGWO-PSO-RF) based intrusion detection model for large-scale attacks in IoT environment.

Scientific reports·2026

Same author

Deep learning approaches for diagnosing seizure based on EEG signal analysis.

Frontiers in human neuroscience·2025

Same author

An AI-powered smart Agribot for detecting locusts in farmlands using IoT and deep learning.

Scientific reports·2025

Same author

GAN-AVI: facial expression translator in Twitter avatar analogous to tweet sentiments.

Scientific reports·2025

Same author

Next generation sequencing under attack: investigating insider threats and organizational behaviour.

PeerJ. Computer science·2025

Same author

Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning.

Sensors (Basel, Switzerland)·2025

Search research articles

相关实验视频

Updated: May 30, 2025

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

使用密集连接的卷积网络进行增强的脑瘤检测和细分,并使用堆叠集体学习.

Asadullah Shaikh¹, Samina Amin², Muhammad Ali Zeb²

¹Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.

Computers in biology and medicine

|January 25, 2025

概括

这项研究介绍了SEL-DenseNet201,这是一个先进的堆叠集体学习模型,用于使用MRI扫描精确的脑瘤 (BT) 分段. 该模型实现了高精度,为早期BT检测和改善患者结果提供了一个有前途的工具.

关键词:

大脑瘤是什么?卷积神经网络是一种卷积神经网络.在DenseNet201中,我们可以使用这就是为什么MRI是MRI.移动网络-v3 移动网络v3 在ResNet50中,我们可以使用ResNet50 分段化分段化分段化分段化在VGG-19中,VGG-19是最重要的.

更多相关视频

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

相关实验视频

Last Updated: May 30, 2025

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

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

科学领域:

医学成像分析分析医学成像分析
为医疗保健提供深度学习.
计算神经科学是一种计算神经科学.

背景情况:

大脑瘤 (BT) 显著影响人类健康,需要准确和早期检测才能有效治疗.
磁共振成像 (MRI) 对于 BT 诊断和细分至关重要,但在实现高精度方面仍然存在挑战.
深度学习,特别是转移学习,在医疗图像分析方面表现有前途,但BT细分仍然很复杂.

研究的目的:

用MRI提高脑瘤检测和细分的准确性和有效性.
实施先进的堆叠集体学习 (SEL) 方法,以提高 BT 分段性能.
评估新型SEL-DenseNet201模型在提高BT细分精度方面的有效性.

主要方法:

设计了一种堆叠集体学习 (SEL) 方法,使用堆叠的DenseNet201作为元模型 (SEL-DenseNet201).
该SEL-DenseNet201模型被六个不同的基本模型所补充:MobileNet-v3,3D-CNN,VGG-16,VGG-19,ResNet50和AlexNet.
该模型在脑瘤MRI数据集上进行了训练,并结合了增强技术以提高性能和强度.

主要成果:

拟议的SEL-DenseNet201模型实现了高精度的99.65%.
该模型显示,脑瘤细分的显著子系数为97.43%.
这些结果表明,与现有的脑瘤细分方法相比,其性能优越.

结论:

该SEL-DenseNet201模型代表了大脑瘤细分精度的重大进步.
这项研究强调了堆叠集体学习在改进基于深度学习的医疗图像分析方面的潜力.
开发的模型显示承诺作为早期脑瘤检测的初始查方法,其成功率很高.