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

相关实验视频

Updated: Jan 7, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

530

通过GA选择的CNN组合进行自动DFU检测,并具有Grad-CAM可解释性.

Shreya Girotra1, Achin Jain1, Sarita Yadav1

  • 1Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.

Scientific reports
|December 14, 2025
PubMed
概括

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

CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease Classification Using Chest X-Ray Images in Federated Learning.

Diagnostics (Basel, Switzerland)·2026
Same author

Geneticization studies as a theoretical field: an uneven history and conceptual reconstruction.

History and philosophy of the life sciences·2026
Same author

An intelligent gradient-guided hybrid inpainting framework for brain MRI reconstruction and Alzheimer's disease classification in connected healthcare systems.

Frontiers in medicine·2026
Same author

Electrocatalytic Water Oxidation by a Heterogenized Co (III)-Aminophenol Complex: Structure-Activity Relationship and DFT Mechanistic Insights.

Chemistry, an Asian journal·2026
Same author

Correction: Comparative evaluation of deep learning models for lung segmentation in chest X-rays: applications in infectious disease screening.

BMC infectious diseases·2026
Same author

Rehab-DRLX: explainable neurorehabilitation prognosis using deep reinforcement learning and transformer-based models.

Frontiers in computational neuroscience·2026

这项研究引入了一种优化的深度学习模型,用于检测糖尿病足 (DFU). 该模型实现了97%的准确性,改善了检测和解释性,以获得更好的患者结果.

科学领域:

  • 医疗图像分析 医学图像分析
  • 医疗保健中的人工智能
  • 糖尿病并发症 糖尿病并发症

背景情况:

  • 糖尿病足 (DFU) 是糖尿病的严重并发症,通常导致截肢和显著的死亡率.
  • 目前用于DFU检测的深度学习模型在优化准确性和可解释性方面面临挑战.
  • 机器学习的进步为通过医学图像分析改善DFU检测提供了潜力.

研究的目的:

  • 开发和验证一个优化的深度学习模型,以准确和可解释地检测糖尿病足 (DFU).
  • 通过将卷积神经网络 (CNN) 结构与遗传算法 (GA) 集合集成,提高DFU检测模型的性能.
  • 通过使用像Grad-CAM这样的可视化技术来提高DFU检测模型的透明度.

主要方法:

  • 开发了一个定制的卷积神经网络 (CNN) 架构,使用七个不同的优化器进行训练.
  • 利用基因算法 (GA) 来从表现最好的个人CNN模型中创建一个整体模型.
  • 实施了Grad-CAM (梯度加权类激活映射) 以实现模型可解释性和决策透明度.

主要成果:

  • 基于GA的组合模型实现了高性能指标:97%的准确性,95%的精度,99%的回忆率和97%的F1得分.
  • 整体模型在DFU检测准确性和稳定性方面表现优于单个单个模型.

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – 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

1.6K

相关实验视频

Last Updated: Jan 7, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

530
Author Spotlight: Advancing Alzheimer's Research – 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

1.6K
  • Grad-CAM成功地可视化了与相关的特征,为模型的决策过程提供了洞察力.

    • 结论:

    • 拟议的优化深度学习模型,结合CNN和GA合奏,显著提高了DFU检测准确性和可解释性.
    • Grad-CAM提高了模型透明度,为医疗保健专业人员在临床决策中提供了有价值的见解.
    • 这种方法代表了利用人工智能来管理糖尿病脚部并发症的有希望的进步.