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相关概念视频

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Classification of Leukocytes01:30

Classification of Leukocytes

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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...
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Aggregates Classification01:29

Aggregates Classification

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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...
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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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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 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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相关实验视频

Updated: Jul 24, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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基于深度学习的皮肤损伤多类分类与全球平均水平的聚合改善

Paravatham V S P Raghavendra1, C Charitha2, K Ghousiya Begum3

  • 1School of Mechanical Engineering, SASTRA Deemed to be University, 613401, Thanjavur, India.

Journal of digital imaging
|July 5, 2023
PubMed
概括
此摘要是机器生成的。

一个新的深度卷积神经网络 (DCNN) 模型准确地识别和分类皮肤病变. 这种人工智能工具实现了97.20%的准确性,超过了现有的早期皮肤癌检测方法.

关键词:
深度卷积神经网络 (DCNN) 是一个深度卷积神经网络.深度学习是一种深度学习.图形用户界面 (GUI) 是一个在HAM1000000中使用.预测 预测 预测皮肤癌是一种皮肤癌.

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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:

  • 皮肤癌的诊断依赖于视觉检查和活检,它们在准确性和可访问性方面存在局限性.
  • 开发用于皮肤病变分类的自动化系统对于早期检测和改善患者治疗结果至关重要.

研究的目的:

  • 提出一种新的深层卷积神经网络 (DCNN) 模型,用于准确的多类皮肤病变识别和分类.
  • 通过先进的深度学习技术,提高皮肤癌检测的性能.

主要方法:

  • 开发了一种新的DCNN模型,用于皮肤病变分析,该模型结合了全球平均聚合.
  • 用HAM10000数据集,包括七个皮肤病变类别,用于模型培训和验证.
  • 预处理涉及黑帽过以去除工件和重新采样以进行数据平衡.

主要成果:

  • 拟议的DCNN模型在多类皮肤病变分类中实现了最高准确率97.20%.
  • 性能与已建立的转移学习模型 (如ResNet50,VGG-16,MobileNetV2和DenseNet121.1) 相比进行了基准测试.
  • 模型的有效性通过使用图形用户界面 (GUI) 进行视觉确认.

结论:

  • 与现有的最先进的方法相比,开发的DCNN模型在自动化皮肤病变分类方面表现出卓越的性能.
  • 这种由人工智能驱动的工具显示出作为皮肤科医生的计算机辅助诊断辅助工具的巨大潜力.
  • 这些发现支持在医疗诊断中深度学习应用的进步,以改善医疗保健.