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相关概念视频

Classification of Connective Tissues01:30

Classification of Connective Tissues

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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
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Phases of Wound Repair01:28

Phases of Wound Repair

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Following injury, the integrity of the injured tissues must be reestablished. For example, in skin tissue, wound repair involves coordination among resident skin cells, blood mononuclear cells, extracellular matrix, growth factors, and cytokines to complete the healing cascade.
Formation of Blood Clot
In case of deep injuries, trauma to blood vessels results in blood loss. In the meantime, phospholipids released from the ruptured endothelial cellular membrane are converted into arachidonic...
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Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

Updated: Sep 17, 2025

Live Imaging of Chemokine Receptors in Zebrafish Neutrophils During Wound Responses
06:48

Live Imaging of Chemokine Receptors in Zebrafish Neutrophils During Wound Responses

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Eff-ReLU-Net:一个深度学习框架,用于多类伤口分类.

Sifat Ullah1, Ali Javed1, Muteb Aljasem2

  • 1Department of Software Engineering, University of Engineering and Technology-Taxila, Taxila, 47050, Pakistan.

BMC medical imaging
|July 2, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,Eff-ReLU-Net,准确地分类慢性伤口,改善患者护理. 这种自动化的伤口分类系统提高了医疗保健专业人员的诊断可靠性和效率.

关键词:
慢性伤口的分类 慢性伤口的分类这就是Eff-ReLU-Net.有效的网 效率的网美国Medetec公司修正后的学习单元.

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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相关实验视频

Last Updated: Sep 17, 2025

Live Imaging of Chemokine Receptors in Zebrafish Neutrophils During Wound Responses
06:48

Live Imaging of Chemokine Receptors in Zebrafish Neutrophils During Wound Responses

Published on: December 4, 2020

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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科学领域:

  • 医学技术 医学技术 医学技术
  • 医疗保健中的人工智能
  • 计算生物学是一种计算生物学.

背景情况:

  • 慢性伤口对健康构成重大风险,包括感染和截肢.
  • 越来越多的患病率需要自动伤口评估,以减少对手工方法的依赖.
  • 准确和快速的伤口分类对于有效的治疗至关重要.

研究的目的:

  • 开发一种高效可靠的深度学习模型,用于多类慢性伤口分类.
  • 改进现有的EfficientNet-B0架构,以进行增强的功能提取.
  • 在各种伤口数据集上验证模型的性能.

主要方法:

  • 提出了Eff-ReLU-Net,这是一个基于EfficientNet-B0的模型,包含ReLU激活和额外的密集层.
  • 采用数据增强技术,包括旋转和翻译,以增强模型概括性.
  • 在AZH和Medetec伤口数据集上评估模型性能,并进行跨体分析.

主要成果:

  • 在这两个数据集上,Eff-ReLU-Net实现了高性能指标.
  • 在Medetec数据集上获得了92.33%的准确性,97.66%的精度,95.33%的回忆率和96.48%的F1分数.
  • 在AZH数据集上获得了90%的准确性,89.45%的精度,92.19%的回忆率和90.84%的F1分数.

结论:

  • 拟议的Eff-ReLU-Net在分类慢性伤口方面表现出显著的有效性.
  • 该模型的架构和增强策略有助于强大的性能和通用性.
  • 使用Eff-ReLU-Net的自动伤口分类为临床实践提供了可靠的解决方案.