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相关概念视频

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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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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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The important convolution properties include width, area, differentiation, and integration properties.
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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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DL-PCMNet:分布式学习启用平行卷积记忆网络,用于用皮肤镜图像进行皮肤癌分类.

Afnan M Alhassan1, Nouf I Altmami1

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia.

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概括
此摘要是机器生成的。

一个新的分布式学习启用并行卷积记忆网络 (DL-PCMNet) 模型使用深度学习准确地分类皮肤癌. 这种方法克服了现有的皮肤病变分类技术的局限性,提高了诊断准确度.

关键词:
深度学习是一种深度学习.皮肤镜图像 皮肤镜图像分布式学习是一种分布式的学习.医学成像医学成像皮肤癌的分类 皮肤癌的分类

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科学领域:

  • 皮肤病学和医学成像学
  • 人工智能在医学中的应用
  • 计算病理学计算病理学

背景情况:

  • 皮肤癌是一种快速传播的致命疾病,其特点是皮肤细胞异常生长.
  • 从皮肤镜像中分类皮肤病变和诊断瘤存在重大挑战.
  • 现有的诊断方法存在数据不足,计算复杂性,类不平衡和性能差等问题.

研究的目的:

  • 为了引入一个先进的模型,有效地分类皮肤癌.
  • 解决当前方法在准确性和可靠性方面的局限性.
  • 通过深度学习改进皮肤病变的诊断.

主要方法:

  • 分布式学习启用并行卷积记忆网络 (DL-PCMNet) 模型的开发.
  • 整合分布式学习以提高灵活性和可靠性.
  • 结合卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 进行强大的特征提取和依赖性捕获.
  • 应用先进的预加工和特征提取技术.

主要成果:

  • 在ISIC 2019数据集上,DL-PCMNet模型实现了高性能.
  • 在90%的训练中实现了97.28%的准确性,97.30%的精度,97.17%的灵敏度和97.72%的特异性.
  • 与现有的皮肤癌分类模型相比,其表现优越.

结论:

  • 拟议的DL-PCMNet模型为皮肤癌分类提供了有效和准确的解决方案.
  • 这种深度学习方法有效地克服了以前的诊断挑战.
  • 该模型显示了改善皮肤病诊断的巨大潜力.