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

Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

5.9K
At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
5.9K

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 method for forensic-ready intrusion detection using explainable learning, prediction-aware graph modeling, and counterfactual analysis.

MethodsX·2026
Same author

Multimodal Fusion of Endoscopic and Histopathological Images for Lesion Detection Using Hybrid Deep Learning.

Current medical imaging·2026
Same author

RETRACTED: Srivastava et al. Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier. <i>Sensors</i> 2022, <i>22</i>, 3620.

Sensors (Basel, Switzerland)·2026
Same author

Deep learning-based region merging with adaptive threshold optimization for building segmentation in remote sensing images.

PloS one·2026
Same author

Stacking Deep Neural Networks to Detect Multiple Types of Cardiac Arrhythmias.

IEEE journal of biomedical and health informatics·2026
Same author

Honey yield prediction and neonicotinoid risk assessment utilizing a machine learning framework in smart agriculture.

Scientific reports·2026

相关实验视频

Updated: Jun 13, 2025

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

2.7K

基于移动网络使用转移学习的白内障和青光眼的检测.

Sheikh Muhammad Saqib1, Muhammad Iqbal2, Muhammad Zubair Asghar2

  • 1Department of Computing and Information Technology, Gomal University, D.I.Khan 29050, Pakistan.

Heliyon
|September 16, 2024
PubMed
概括

早期发现白内障和绿内障等眼病对于预防失明至关重要. 这项研究引入了优化的移动网络模型,用于准确,自动检测疾病,优于现有方法.

关键词:
以及MobilNet的使用情况.深度学习是一种深度学习.机器学习是机器学习.这就是ResNet ResNet.转移学习转移学习植物网 (VeggNet) 是一个植物网.

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

相关实验视频

Last Updated: Jun 13, 2025

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

2.7K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

科学领域:

  • 眼科医生 眼科 眼科
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 白内障和绿内障是导致失明的主要原因.
  • 早期和准确的诊断对于有效的治疗和降低风险至关重要.
  • 机器学习和深度学习对自动眼睛疾病检测充满希望.

研究的目的:

  • 开发和评估轻量级深度神经网络,用于自动检测白内障和绿内障.
  • 将拟议的MobileNetV1和MobileNetV2模型的性能与其他深度学习架构进行比较.

主要方法:

  • 利用深度可分离的卷积来构建优化,轻量级的深度神经网络.
  • 使用的是MobileNetV1和MobileNetV2架构.
  • 在公开可用的白内障/正常和绿内障/正常图像数据集上训练和测试模型.

主要成果:

  • 拟议的MobileNetV1和MobileNetV2模型在检测白内障和绿内障方面取得了最高的准确性.
  • 与其他评估的深度学习模型相比,优化的架构表现出更高的性能.

结论:

  • 轻量级深度神经网络,特别优化的MobileNet架构,对于自动化白内障和青光眼的检测非常有效.
  • 这种方法为早期诊断和预防由这些眼睛疾病引起的失明提供了有希望的工具.