Jove
Visualize
联系我们

相关概念视频

Skin Cancer01:30

Skin Cancer

3.0K
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...
3.0K

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

Home-based respiratory-gated transcutaneous auricular vagus nerve stimulation for rheumatoid arthritis-a feasibility study.

Clinical rheumatology·2026
Same author

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms: Phase 2.

Sensors (Basel, Switzerland)·2026
Same author

Evaluation of In-Ear and Fingertip-Based Photoplethysmography Sensors for Measuring Cardiac Vagal Tone Relevant Heart Rate Variability Parameters.

Sensors (Basel, Switzerland)·2025
Same author

Development of an eHealth Intervention Including Self-Management for Reducing Sedentary Time in the Transition to Retirement: Participatory Design Study.

JMIR formative research·2025
Same author

Automated Food Weight and Content Estimation Using Computer Vision and AI Algorithms.

Sensors (Basel, Switzerland)·2024
Same author

Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining.

Sensors (Basel, Switzerland)·2024
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

相关实验视频

Updated: May 28, 2025

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

2.2K

在计算机辅助皮肤癌检测中用于黑色素瘤诊断的高级深度学习模型.

Ranpreet Kaur1, Hamid GholamHosseini2, Maria Lindén3

  • 1Department of Software Engineering & AI, Media Design School, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括

早期检测黑色素瘤,最致命的皮肤癌,至关重要. 这项研究开发了一种计算机辅助诊断 (CAD) 的自动化模型,通过皮肤镜图像对黑色素瘤进行分类,其准确度达到93.40%.

关键词:
这是分类分类的分类.深度学习是一种深度学习.黑色素瘤是一种黑色素瘤.细分化 细分化的细分化皮肤癌是皮肤癌.

更多相关视频

A 3D Organotypic Melanoma Spheroid Skin Model
08:49

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

15.6K
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

2.6K

相关实验视频

Last Updated: May 28, 2025

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

2.2K
A 3D Organotypic Melanoma Spheroid Skin Model
08:49

A 3D Organotypic Melanoma Spheroid Skin Model

Published on: May 18, 2018

15.6K
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

2.6K

科学领域:

  • 皮肤病学 皮肤病学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 黑色素瘤是最致命的皮肤癌,由于视觉检查的局限性,经常被诊断为晚期.
  • 早期检测和及时治疗对于限制黑色素瘤的严重程度至关重要.

研究的目的:

  • 开发有效的计算机辅助诊断 (CAD) 方法来检测黑色素瘤.
  • 增强预处理,细分和分类阶段,以提高诊断准确度.

主要方法:

  • 一种混合方法,将形态操作和深度神经网络结合起来,用于图像预处理 (去除毛线,增强对比度).
  • 一个基于深度学习的细分网络来隔离损伤区域.
  • 一个深度神经网络用于使用ISIC 2020数据集对黑色素瘤与良性病变进行分类.

主要成果:

  • 包含预处理和细分的分类模型实现了93.40%的准确率.
  • 该模型展示了每张图像1.3秒的高效测试时间.
  • 在使用清理和分段图像与原始图像时,比较显示出更高的性能.

结论:

  • 自动化CAD模型显著提高了早期皮肤癌检测的准确性.
  • 预处理和细分是提高黑色素瘤分类性能的关键步骤.
  • 提出的方法为协助皮肤科医生在黑色素瘤诊断方面提供了一个有前途的工具.