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

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

A multi-epitope pan-betacoronavirus vaccine construct predicted to induce broad-spectrum and durable immune responses: an immunoinformatics approach.

Frontiers in bioinformatics·2026
Same author

Synthesis and characterization of natural biocomposite scaffold for bone repair: A study of gelatin-hydroxyapatite from Aceh-sourced bovine bone.

Dental materials journal·2026
Same author

Enhanced Magnesium Ion Sensing Using Polyurethane Membranes Modified with ĸ-Carrageenan and D2EHPA: A Potentiometric Approach.

Biosensors·2026
Same author

Network Pharmacology-Based Identification of Key Metabolites from Immunized <i>Rhynchophorus</i> Larvae as Therapeutic Agents Against Antibiotic-Resistant <i>Neisseria gonorrhoea</i>.

Pakistan journal of biological sciences : PJBS·2025
Same author

Mechanistic insights into the anticancer, anti-inflammatory, and antioxidant effects of yellowfin tuna collagen peptides using network pharmacology.

Narra J·2025
Same author

Molecular insights into atopic dermatitis treatment: investigating bioactive compounds in Aceh's traditional fermented coconut oil.

Journal of biomolecular structure & dynamics·2025

相关实验视频

Updated: Jun 2, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

947

牛皮严重程度评估:通过深度学习优化诊断模型.

Aga Maulana1,2, Teuku R Noviandy1, Rivansyah Suhendra3

  • 1Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia.

Narra J
|January 16, 2025
PubMed
概括

深度学习模型可以准确地分类牛皮的严重程度. ResNet50获得了92.50%的准确性,为客观的牛皮评估和改善治疗计划提供了潜力.

关键词:
在过去的时间里,PASI PASI深度学习是一种深度学习.诊断模型 诊断模型 诊断模型牛皮是一种牛皮.皮肤病的分类 皮肤病的分类

更多相关视频

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

334
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

相关实验视频

Last Updated: Jun 2, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

947
Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
02:28

Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients

Published on: March 1, 2024

334
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

科学领域:

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

背景情况:

  • 牛皮的严重程度评估是具有挑战性的,因为微妙的视觉差异.
  • 准确的分类对于有效的治疗计划至关重要.

研究的目的:

  • 评估用于自动化牛皮严重程度分类的深度学习模型.
  • 为此任务确定最佳的深卷积神经网络 (DCNN).

主要方法:

  • 预先处理了1546张牛皮图像的数据集,并将其分类为四个严重程度 (没有,轻度,中度,严重).
  • 五个DCNN (ResNet50,VGGNet19,MobileNetV3,MnasNet,EfficientNetB0) 进行了培训和验证,其中包括:
  • 性能指标包括准确度,精度,灵敏度,特异性和F1分数,并进行统计测试进行比较.

主要成果:

  • ResNet50以92.50%的准确度表现出卓越的性能.
  • ResNet50实现了高精度 (93.10%),灵敏度 (92.50%),特异性 (97.37%) 和F1得分 (92.68%).

结论:

  • ResNet50显示了对持续和客观的牛皮严重程度评估的巨大潜力.
  • 这种自动化方法可以帮助皮肤科医生进行诊断和治疗计划.
  • 建议进行进一步的临床验证,以便广泛采用.