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

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

7.2K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
7.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

807
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
807
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.6K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.6K

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 journal

Synergistic Deep Learning Fusion for Precision Lung Cancer Staging.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Genomic Landscape of Oral Squamous Cell Carcinoma in Never Smokers and Never Drinkers.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Gut Microbiota Modulation via Synbiotics: A Perspective for Boosting Antitumor Immunity and Inactivating Carcinogens in Early Life.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Temporal Hematologic Alterations in Women Receiving Pharmacotherapy for Breast Cancer: A Prospective Analysis.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Upstaging of Operable Adenocarcinoma of the Stomach and Gastroesophageal Junction Following Staging Laparoscopy (SL): High-Risk Clinicopathological Features Requisite for Mandatory SL.

Asian Pacific journal of cancer prevention : APJCP·2026
Same journal

Gene Expression Alterations of TIMP3, ELASTIN, K-RAS, and BRAF in Colorectal Cancer Patients with H. pylori Infection.

Asian Pacific journal of cancer prevention : APJCP·2026
查看所有相关文章

相关实验视频

Updated: Mar 9, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

骨癌细胞预测使用增强的深度学习算法与优化技术.

Mohanthi Kakarla1, K Padma Raju2

  • 1Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Asian Pacific journal of cancer prevention : APJCP
|March 7, 2026
PubMed
概括
此摘要是机器生成的。

一个新的CS-MHC ResNet模型显著提高了自动骨癌检测准确度. 这种深度学习方法增强了特征选择和分类,为早期诊断提供了更可靠的工具.

关键词:
卷积神经网络 (CNN) 是一种神经网络.子搜索优化 (CSO) 是一种搜索优化.机器学习 (ML) 是指机器学习.支持矢量机器 (SVM) 是一个支持矢量机器.视觉几何学小组 (VGG)

相关实验视频

Last Updated: Mar 9, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

科学领域:

  • 医学成像分析分析 医学成像分析
  • 计算瘤学是一种计算瘤学.
  • 机器学习在医疗保健中的应用

背景情况:

  • 早期发现骨癌对于患者的生存至关重要.
  • 传统的诊断方法耗时,需要专门的专业知识.
  • 需要自动化系统来提高骨癌诊断的准确性和效率.

研究的目的:

  • 开发一种机器学习 (ML) 驱动的工具,用于增强骨癌检测和分类.
  • 通过将深度学习 (DL) 与优化算法集成来提高诊断准确性.
  • 创建一个临床适用模型,用于早期骨癌诊断.

主要方法:

  • 采用了混合方法,将ResNet与Cuckoo Search修改登 (CS-MHC) 优化相结合.
  • 使用Cuckoo搜索优化 (CSO) 进行功能选择和超参数调整.
  • 将CS-MHC ResNet模型与传统DL模型 (VGG-16,Inception,Xception) 进行比较.

主要成果:

  • CS-MHC ResNet实现了更高的分类准确度 (超过90%),灵敏度 (约85%),精度 (超过88%) 和F-测量 (大约1. 86%) 的情况.
  • 集成的CSO增强了特征选择,提高了骨癌分类的有效性.
  • 超越了传统模型,证明了混合优化和DL方法的有效性.

结论:

  • CS-MHC ResNet 模型在自动化骨癌检测方面取得了重大进展.
  • 该模型显示了临床应用的高潜力,提供了一个更有效和可靠的诊断工具.
  • 未来的工作将专注于使用更大的数据集进行验证,并探索更简单的模型设计以获得更广泛的适用性.