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相关概念视频

Clinical Applications of Epidermal Stem Cells01:19

Clinical Applications of Epidermal Stem Cells

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Epidermal stem cells (EpiSCs) are mainly located at the basal layer of the epidermis. These cells repair minor injuries of the skin and replace dead skin cells. However, EpiSCs’ cannot heal severe wounds such as major burns or those from diabetes or hereditary disorders. In such cases, culturing the epidermal stem cells from the patient is possible and has yielded successful treatment options, such as laboratory-grown skin grafts. These grafts are synthesized using a patient’s own...
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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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Renewal of Skin Epidermal Stem Cells01:12

Renewal of Skin Epidermal Stem Cells

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The skin is divided into epidermis, dermis, and hypodermis, the skin's outermost, middle, and inner layers. The human epidermal layer regularly undergoes renewal, where old, dead cells are replaced by new cells. Epidermal stem cells or EpiSCs divide and differentiate to restore the lost cells. For the renewal process, some EpiSCs continuously self-renew. In contrast, few others differentiate into transit-amplifying cells, which later form prickle or spinous cells, followed by granular...
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相关实验视频

Updated: Jul 8, 2025

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通过优化分布式囊神经网络检测表皮病变.

Vineet Kumar Dubey1, Vandana Dixit Kaushik1

  • 1Department of Computer Science & Engineering, Harcourt Butler Technical University, Kanpur, 208002, India.

Computers in biology and medicine
|December 10, 2023
PubMed
概括

一个新的基于黄金优化的分布式囊神经网络 (GHO-DCaNN) 提高了皮肤癌检测的准确性. 这种方法提高了皮肤病变的早期诊断,包括黑色素瘤,以获得更好的患者结果.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 包括黑色素瘤在内的皮肤癌对全球健康构成重大风险,早期诊断对生存至关重要.
  • 由于症状的复杂性和可变性,准确诊断皮肤病变仍然具有挑战性.
  • 现有的诊断方法需要改进,以提高准确性和效率.

研究的目的:

  • 引入一种新的计算方法,用于增强皮肤癌检测.
  • 提高诊断皮肤病变,特别是黑色素瘤的准确性和可靠性.
  • 开发一个优化的深度学习模型,用于精确地识别表皮损伤.

主要方法:

  • 开发了一个基于黄金优化的分布式囊神经网络 (GHO-DCaNN).
  • 使用下水道飞优化 (SSFO) 进行了基于集群的优化细分方法.
  • GHO-DCaNN模型是使用灵感来自金和火行为的混合GHO优化器进行训练的.

主要成果:

  • 在GHO-DCaNN实现了皮肤病变检测的高性能指标.
  • 具体性,灵敏度和准确率达到97.53%,99.05%和98.83%,90%的训练数据.
  • 通过十倍的交叉验证,性能提高到97.83%的特异性,99.50%的灵敏性和99.06%的准确性.
关键词:
分布式囊神经网络 分布式囊神经网络表皮病变检测检测表皮病变的检测方法黄金优化优化 黄金优化混合型深度描述器混合型深度描述器混合四元格图案 混合四元格图案

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结论:

  • 拟议的GHO-DCaNN显示了准确和高效的皮肤癌诊断的巨大潜力.
  • 集成先进的优化算法增强了病变细分和特征提取.
  • 这种计算方法在皮肤医学医学成像领域提供了有前途的进步.