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训练人工神经网络使用自我组织的迁移算法进行皮肤细分.

Quoc Bao Diep1, Thanh-Cong Truong2, Ivan Zelinka3,4

  • 1Faculty of Mechanical - Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam. bao.dq@vlu.edu.vn.

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概括
此摘要是机器生成的。

自组织迁移算法 (SOMA) 训练了人工神经网络进行皮肤细分,达到93.18%的准确性. 这种进化方法超过了基于梯度的方法和差异进化以改善图像细分.

关键词:
人工神经网络的人工神经网络计算机视觉 计算机视觉 计算机视觉优化算法优化算法这就是SOMAOMA.皮肤细分 皮肤细分团结情报团队的人群.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 精确的皮肤细分对于各种应用至关重要,包括医学成像和增强现实.
  • 训练人工神经网络 (ANN) 通常依赖于基于梯度的优化,这可能会面临诸如局部优化等挑战.
  • 进化算法为训练复杂模型提供了替代优化策略.

研究的目的:

  • 评估自组织迁移算法 (SOMA) 的有效性,用于训练ANN进行皮肤细分.
  • 为了比较SOMA的性能与已建立的梯度基础优化器 (ADAM,SGDM) 和另一个进化算法 (DE).

主要方法:

  • 应用了自组织迁移算法 (SOMA) 来训练ANN进行皮肤细分.
  • 使用皮肤数据集 (245,057个样本) 与ADAM,SGDM和差异演变 (DE) 进行基准测试.
  • 进行了定量准确度指标和定性视觉评估.

主要成果:

  • 经过SOMA训练的ANN实现了最高的准确率93.18%.
  • 索马的表现明显超过了ADAM (84.87%),SGDM (84.79%) 和DE (91.32%).
  • 视觉评估证实了SOMA训练模型可靠的细分能力.

结论:

  • 自组织迁移算法 (SOMA) 是一个高效的优化器,用于训练ANN进行皮肤细分任务.
  • 进化优化为改善图像分割性能提供了基于梯度的方法的有希望的替代方案.
  • 索马展示了提高计算机视觉深度学习模型的准确性和可靠性的潜力.