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基于卷积神经网络的听力损失分类模型的性能比较,使用听觉脑干响应数据.

Jun Ma1, Seong Jun Choi2, Sungyeup Kim3

  • 1Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

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概括
此摘要是机器生成的。

这项研究表明,深度学习模型AlexNet能够准确地从听觉脑干响应 (ABR) 图像中分类听力损失,准确率为95.93%,有助于自动诊断.

关键词:
这就是ABR ABR.亚历克斯的网络亚历克斯的网络密集的网络121在 Densenet201上,我们可以看到在VGG16中,VGG16是VGG16中的一个.在VGG19中,VGG19是VGG19的代表.听觉脑干反应的响应深度学习是一种深度学习.听力损失 听力损失是什么图像处理是图像处理的过程.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 听力学 听力学是指听力学.

背景情况:

  • 听力损失诊断依赖于听觉脑干响应 (ABR) 数据.
  • 自动化ABR分析可以提高诊断效率.

研究的目的:

  • 用ABR图像评估卷积神经网络 (CNN) 模型用于听力损失分类.
  • 为了比较六个CNN架构的性能:VGG16,VGG19,DenseNet121,DenseNet-201,AlexNet和InceptionV3.3的性能.

主要方法:

  • 使用了7990个预处理ABR图像的数据集.
  • 系统测试了六个CNN模型的分类准确性.
  • 基于准确性和计算效率进行了比较分析.

主要成果:

  • 亚历克斯网在分类听力损失方面实现了最高准确率的95.93%.
  • 在ABR图像分析中证明了深度学习模型的有效性.

结论:

  • 深度学习,特别是AlexNet,显示出从ABR图表中自动化听力损失诊断的巨大潜力.
  • 进一步完善模型可以提高临床应用和诊断准确性.