Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
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 JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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

Enhancing urban air quality prediction using time-based-spatial forecasting framework.

Scientific reports·2025
Same author

Comparison of the effectiveness of butterfly arch versus transpalatal arch in anchorage reinforcement: A linear 3D finite element study.

Journal of dental research, dental clinics, dental prospects·2022
Same author

Breastfeeding and its Association with Early Childhood Caries - An Umbrella Review.

The Journal of clinical pediatric dentistry·2022
Same author

Seed Biopriming With <i>Trichoderma</i> Strains Isolated From Tree Bark Improves Plant Growth, Antioxidative Defense System in Rice and Enhance Straw Degradation Capacity.

Frontiers in microbiology·2021

相关实验视频

Updated: Jul 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K

脑电图扫描胰腺癌细分和分类使用深度学习和衣群算法.

Hari Prasad Gandikota1, Abirami S1, Sunil Kumar M2

  • 1Department of Computer Science & Engineering, Annamalai University, Chidambaram, Tamilnadu, India.

PloS one
|November 6, 2023
PubMed
概括

这项研究引入了一种新的深度学习技术,用于CT扫描中的胰腺癌 (PC) 分类. 通过结合先进的细分和分类模型,TSADL-PCSC方法提高了诊断的准确性.

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:

  • 胰腺癌 (PC) 是一种高度致命的疾病,存活率很低,需要准确及时诊断.
  • 计算机断层扫描 (CT) 扫描对于可视化胰腺组织至关重要,有助于区分癌症和非癌症区域.
  • 机器学习 (ML) 和深度学习 (DL) 在自动化和改进来自CT扫描的PC分类方面表现有前途.

研究的目的:

  • 开发一个准确和高效的深度学习模型,用于CT扫描中的胰腺癌细分和分类.
  • 通过自动化方法提高胰腺癌检测的诊断性能.

主要方法:

  • 开发了基于深度学习的胰腺癌细分和分类 (TSADL-PCSC) 技术的 Tunicate Swarm 算法.
  • 在CT扫描中,W-Net被用于精确细分受影响的胰腺区域.
  • 幽灵网作为特征提取器,并使用深回声状态网络 (DESN) 来进行分类,超参数由Tunicate Swarm算法 (TSA) 调整.

主要成果:

  • 与现有方法相比,TSADL-PCSC技术在胰腺癌分类准确度方面取得了显著的改进.
  • 在基准CT扫描数据库上的实验结果验证了拟议方法的有效性.

结论:

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

435
Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models
06:57

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Published on: April 7, 2018

10.9K

相关实验视频

Last Updated: Jul 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

435
Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models
06:57

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models

Published on: April 7, 2018

10.9K
  • TSADL-PCSC技术为使用CT扫描进行胰腺癌分类提供了一个有希望,准确和自动化的解决方案.
  • 这种先进的深度学习方法有可能显著改善胰腺癌患者的诊断结果.