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

Skin Cancer01:30

Skin Cancer

4.2K
Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
4.2K

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

Association Between HDL Cholesterol Changes and Cardiovascular Event Risk: A Nationwide Health Screening Cohort in Japan.

Healthcare (Basel, Switzerland)·2026
Same author

Risk of arrhythmia following ankylosing spondylitis, 2012-2023: a nationwide cohort study.

Clinical rheumatology·2026
Same author

Measles Epidemiology, Transmission, and Surveillance Characteristics in Ethiopia, 2018-2024.

Journal of epidemiology and global health·2026
Same author

Time-dependent risk of sleep disorders in patients with epilepsy: a nationwide cohort study.

BMC neurology·2026
Same author

Sequential Transfer Learning for Multi-Domain Breast Image Segmentation Using a Transformer-Enhanced Hybrid U-Net.

Bioengineering (Basel, Switzerland)·2026
Same author

Long-Term Risk of Parkinson's Disease Following Irritable Bowel Syndrome: A Nationwide Population-Based Cohort Study.

Healthcare (Basel, Switzerland)·2026

相关实验视频

Updated: Jul 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

557

提高皮肤损伤检测:一个多阶段的多类卷积神经网络基础框架.

Muhammad Umair Ali1, Majdi Khalid2, Hanan Alshanbari2

  • 1Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)
|December 23, 2023
PubMed
概括

这项研究引入了一个深度学习框架,用于分类皮肤病变. 新的多阶段方法可以准确识别良性和恶性类型,改善早期皮肤病诊断.

关键词:
这是分类分类的分类.卷积神经网络是一种卷积神经网络.黑色素瘤是一种黑色素瘤.皮肤癌是皮肤癌.皮肤病变检测 皮肤病变检测

更多相关视频

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.3K
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

6.8K

相关实验视频

Last Updated: Jul 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

557
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.3K
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

6.8K

科学领域:

  • 皮肤病学 皮肤病学
  • 计算机辅助诊断是一种计算机辅助的诊断.
  • 机器学习 机器学习

背景情况:

  • 准确的皮肤病变检测对于及时的皮肤治疗至关重要.
  • 机器学习和计算机辅助诊断在分析皮肤病变方面提供了有前途的进展.

研究的目的:

  • 开发基于深层卷积神经网络 (CNN) 的多阶段框架,用于分类七种类型的皮肤病变.
  • 通过转移学习提高皮肤病变分类的准确性和效率.

主要方法:

  • 设计了一个两阶段的CNN模型,首先将病变分类为良性或恶性.
  • 转移学习在第二阶段被应用,将良性和恶性病变分为特定类型.
  • 从最初的CNN训练中使用结重量来有效地进行转移学习.

主要成果:

  • 该框架在ISIC2018数据集上的良性和恶性病变的分类中实现了93.4%的准确性.
  • 对于良性和恶性病变类型,分类准确率达到96.2%.
  • 与现有的CNN模型相比,拟议的方法显示出更高的分类率和更短的训练时间.

结论:

  • 多阶段,多类的CNN框架有效地分类各种皮肤病变.
  • 这种方法提高了皮肤病应用中的诊断准确性和效率.
  • 该研究强调了转移学习在医学图像分析的深度学习中的潜力.