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

4.0K
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...
4.0K

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

Metabolic Bone Disease of Prematurity: A Preventable Morbidity in Very Low Birth Weight Infants.

Indian journal of public health·2026
Same author

Case Series of Head and Neck Paragangliomas: Radiological Diagnosis and Clinical Characteristics.

Maedica·2026
Same author

A Cross-Sectional Comparative Evaluation of the Forced Oscillation Technique and Spirometry in Patients Suspected of Having Obstructive Airway Disease at a Tertiary Care Centre.

Cureus·2026
Same author

Pulmonologist-performed Ultrasound-guided Transthoracic Biopsy of Pleural-based Lung Masses: Diagnostic Yield and Safety, a Retrospective Study.

Thoracic research and practice·2026
Same author

Co-Administration of LPC and LPS Enhanced the Spinal Cord Vulnerability in a Mouse Model of Focal Demyelination.

Journal of molecular neuroscience : MN·2026
Same author

Exploring Bakuchiol as an HSP90-Targeting Lead Against Triple-Negative Breast Cancer: Evidence from In Silico, In Vitro, and Synergy Studies.

Journal of computer-aided molecular design·2026

相关实验视频

Updated: Jun 17, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:52

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

126

基于变压器的黑色素瘤分类解码器使用手工制作的纹理特征融合和灰狼优化算法.

Hemant Kumar1, Abhishek Dwivedi2, Abhishek Kumar Mishra3

  • 1Department of Information Technology, School of Engineering & Technology, Chhatrapati Shahu Ji Maharaj University, Kanpur, India.

MethodsX
|August 6, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种先进的黑色素瘤分类方法,使用纹理特征和用变压器模型进行灰狼优化 (GWO). 这种方法显著提高了早期皮肤癌检测的准确性.

关键词:
灰狼优化 灰狼优化灰色水平竞争矩阵 (GLCM) 是一种竞争矩阵.线性二进制模式 (LBP) 是一种线性二进制模式.黑色素瘤是一种黑色素瘤.使用特征融合,灰狼优化和基于变压器的编码器进行黑色素瘤分类.多头注意力注意力多头注意力规模点产品的产品规模点.变压器变压器变压器

更多相关视频

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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

相关实验视频

Last Updated: Jun 17, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma
09:52

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

126
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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

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:

  • 黑色素瘤检测对于有效治疗至关重要,需要准确有效的诊断工具.
  • 目前的方法可能缺乏早期黑色素瘤识别所需的精度.
  • 整合先进的计算技术可以提高皮肤癌分类的准确性.

研究的目的:

  • 开发和验证一种用于增强黑色素瘤分类的新方法.
  • 使用混合AI模型提高黑色素瘤检测的效率和准确性.
  • 在变压器框架内利用纹理分析和优化算法.

主要方法:

  • 图像预处理包括中位过以提高质量.
  • 使用灰级共发生矩阵 (GLCM) 和局部二进制模式 (LBP) 提取纹理特征.
  • 通过灰狼优化 (GWO) 选择功能,并使用基于变压器的解码器进行分类.

主要成果:

  • 拟议的方法实现了高精度 (HAM10000上99.54%,ISIC2019上99.47%) 和F1得分 (HAM10000上99.11%,ISIC2019上99.25%).
  • 手工制作的纹理特征与变压器模型的整合证明了黑色素瘤分类的有效性.
  • 在基准数据集上的实验验证证证了该方法的卓越性能.

结论:

  • 开发的基于变压器的模型具有GWO选择的纹理特征,为黑色素瘤检测提供了高度有效的解决方案.
  • 这种方法证明了在临床环境中提高诊断准确性的巨大潜力.
  • 这项研究强调了经典图像特征和用于医学图像分析的现代深度学习架构之间的协同作用.