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相关概念视频

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.
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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.
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
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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.
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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.
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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.
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使用卷积神经网络来分类麻疹皮肤病变.

Entesar Hamed I Eliwa1,2, Amr Mohamed El Koshiry3,4, Tarek Abd El-Hafeez5,6

  • 1Department of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box: 400, 31982, Al-Ahsa, Saudi Arabia. eheliwa@kfu.edu.sa.

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概括

使用卷积神经网络 (CNN) 的新型深度学习方法,并与灰狼优化器 (GWO) 进行了优化,显著提高了麻疹皮肤病变诊断的准确性,达到95.3%. 这种方法增强了早期检测和公共卫生监测麻疹疫情.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 计算生物学 计算生物学

背景情况:

  • 麻疹是一种罕见的病毒性疾病,引起严重的疾病,具有特征性的皮肤病变.
  • 准确的视觉诊断病变是具有挑战性的,特别是在资源有限的环境中.
  • 深度学习,特别是卷积神经网络 (CNN),显示出对图像分类任务的希望.

研究的目的:

  • 开发和评估一个深度学习模型来分类麻疹皮肤病变.
  • 为了优化CNN模型的性能,使用灰狼优化器 (GWO) 算法.
  • 评估优化模型在改善天花诊断和公共卫生监测方面的潜力.

主要方法:

  • 卷积神经网络 (CNN) 的实施用于麻疹皮肤病变的图像分类.
  • 使用灰狼优化器 (GWO) 算法优化CNN模型架构和参数.
  • 使用包括准确性,精度,回忆,F1得分和AUC在内的指标进行性能评估.

主要成果:

  • 优化的CNN模型在分类麻疹皮肤病变方面取得了95.3%的高准确性.
  • 与非优化模型相比,灰狼优化器 (GWO) 显著提高了模型的辨别能力.
  • 通过GWO优化观察到更好的性能指标 (准确性,精度,回忆,F1得分,AUC).

结论:

  • 优化GWO的CNN模型提供了一个非常准确和高效的方法来诊断麻疹.
  • 这种人工智能驱动的方法有可能促进早期检测并改善患者的治疗结果.
  • 这种方法对通过加强监测控制和预防麻疹疫情具有重大公共卫生影响.