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相关概念视频

Classification of Bones01:18

Classification of Bones

5.6K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
5.6K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

56.6K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
56.6K
Classification of Connective Tissues01:30

Classification of Connective Tissues

10.7K
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....
10.7K
Classification of Leukocytes01:30

Classification of Leukocytes

2.0K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.0K
Classification of Systems-II01:31

Classification of Systems-II

151
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,
151
Aggregates Classification01:29

Aggregates Classification

329
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
329

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相关实验视频

Updated: Jul 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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适应的基于深度集体学习的投票分类器用于骨肉瘤癌症分类.

Md Abul Ala Walid1,2, Swarnali Mollick2, Pintu Chandra Shill1

  • 1Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh.

Diagnostics (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了深度学习模型,包括一个新的卷积神经网络 (CNN) 和一个集体投票分类器,以从医学图像中准确地分类骨髓瘤. 开发的模型实现了高精度,有助于癌症诊断.

关键词:
骨恶性瘤是一种恶性瘤.卷积神经网络 (CNN) 是一个神经网络.组合学习组合学习组织病理学图像分类 图像分类骨质肉瘤是骨质肉瘤的一种疾病.转移学习转移学习

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相关实验视频

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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科学领域:

  • 医学成像分析 医学成像分析
  • 计算病理学计算病理学
  • 在瘤学中使用人工智能

背景情况:

  • 骨髓瘤的分类依赖于对H&E染色图像的组织病理学分析.
  • 不均分散的数据集对开发可靠的机器学习模型构成挑战.
  • 数据增强对于增强模型概括至关重要.

研究的目的:

  • 开发和评估深度学习模型,以准确地分类骨髓瘤.
  • 为了解决骨髓瘤图像数据集中的数据不平衡问题.
  • 提高自动诊断工具的可靠性和性能.

主要方法:

  • 利用了血素和素染色骨肉瘤图像的数据集.
  • 采用数据增强技术来提高概括性.
  • 开发了一个新的卷积神经网络 (CNN) 和一个集体投票分类器.
  • 在冷和微调阶段评估了六个预训练的CNN模型 (MobileNetV1,MobileNetV2,ResNetV250,InceptionV2,EfficientNetV2B0,NasNetMobile).

主要成果:

  • 拟议的CNN模型获得了93.09%的卡帕得分,超过了预先训练的模型.
  • 调整后的基于集体学习的异质投票分类器获得了最高的卡帕得分96.50%.
  • 与所有其他评估模型相比,整体模型表现出卓越的性能.

结论:

  • 提出的深度学习模型,特别是集体投票分类器,显示了准确的骨髓瘤分类的巨大潜力.
  • 这些发现对远程医疗,移动医疗保健和支持医疗专业人员进行诊断具有实际意义.
  • 用均分布的培训数据集解决数据不平衡是开发公正学习模型的关键.