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

4.6K
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...
4.6K

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 author

Early Classification of Bladder Cancer Using Spectrum-Aided Visual Enhancer (SAVE) and Deep Learning Models: A Non-Invasive Technology for Faster Detection.

Cancers·2026
Same author

SAVE: Spectrum-Aided Visual Enhancement for AI-Based Skin Cancer Detection.

Diagnostics (Basel, Switzerland)·2026
Same author

LiquidGAN for Handwriting-Based Detection and Severity Classification of Extrapyramidal Symptoms.

Sensors (Basel, Switzerland)·2026
Same author

Use of Waste Banana Stem for the Synthesis of Carboxymethyl Cellulose-Based Coating Formulation for Preservation of Tomatoes.

Chemistry & biodiversity·2026
Same author

From surface to signal: Clinical pathways for transition metal dichalcogenide-based electrochemical biosensors.

Biosensors & bioelectronics·2026
Same author

Efficacy of Spectral-Aided Visual Enhancer in Classification of Esophageal Cancer.

Cancers·2026

相关实验视频

Updated: Sep 13, 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

2.5K

使用机器学习进行增强皮肤癌分类的超光谱成像.

Teng-Li Lin1, Arvind Mukundan2,3, Riya Karmakar2

  • 1Department of Dermatology, Dalin Tzu Chi Hospital, No. 2, Min-Sheng Rd., Dalin Town, Chiayi 62247, Taiwan.

Bioengineering (Basel, Switzerland)
|July 29, 2025
PubMed
概括

一个新的频谱辅助视觉增强器 (SAVE) 系统通过将RGB图像转换为窄带图像来改善皮肤癌的分类. 这增强了可视化,帮助皮肤科医生准确区分行为性角质瘤 (AK),基底细胞癌 (BCC) 和状细胞癌 (SK).

关键词:
频段的选择 频段的选择卷积神经网络是一种卷积神经网络.超光谱成像技术的使用.窄带成像技术的使用.随机的森林随机的森林皮肤癌是皮肤癌.频谱辅助视力增强器 频谱辅助视力增强器这是YOLO的YOLO.

更多相关视频

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

229

相关实验视频

Last Updated: Sep 13, 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

2.5K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

16.9K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

229

科学领域:

  • 皮肤病学和医学成像学
  • 计算病理学计算病理学
  • 医疗保健中的机器学习

背景情况:

  • 精确的皮肤癌的分类,如行动性角质瘤 (AK),基底细胞癌 (BCC) 和状细胞癌 (SK),对于有效的治疗至关重要.
  • 这些疾病的临床区分是具有挑战性的,因为它们表现相似,往往导致误诊.
  • 传统的RGB成像可能缺乏足够的对比度来区分皮肤病变中的微妙差异.

研究的目的:

  • 引入光谱辅助视觉增强器 (SAVE) 系统,以使用高光谱成像 (HSI) 改善皮肤病变的可视化.
  • 评估SAVE在提高AK,BCC和SK分类准确性的有效性.
  • 为了比较SAVE与传统的RGB成像用于皮肤癌诊断的性能.

主要方法:

  • 开发光谱辅助视觉增强器 (SAVE) 系统,该系统使用HSI将RGB图像转换为窄带图像 (NBI).
  • 应用十种机器学习算法 (CNN,RF,YOLOv8,SVM变体,ResNet50,MobileNetV2,物流回归) 来进行病变分类.
  • 基于增强的图像对比度,评估系统区分AK,BCC和SK的能力.

主要成果:

  • SAVE系统显著提高了癌症病变与正常组织的对比度.
  • 与传统的RGB成像相比,分类性能,包括精度,灵敏度和特异性得到了改进.
  • 机器学习算法在使用SAVE处理图像时,在区分AK与BCC和SK方面表现出更高的能力.

结论:

  • 使用HSI的SAVE系统为皮肤科医生提供了一种宝贵的工具,用于早期和准确地诊断皮肤癌.
  • 这种先进的成像方法减少了错误分类的可能性,从而改善了患者管理和结果.
  • 在临床环境中,SAVE为客观和精确的皮肤病变分析提供了一个有前途的方法.