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MLCDL:使用深度学习算法进行多组织分类和诊断的关键实践和实施.

Pijush Dutta1, Amit Dey1, Raushan Das1

  • 1Greater Kolkata College of Engineering and Management, Baruipur, West Bengal, India.

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概括
此摘要是机器生成的。

使用EfficientNet-B7 CNNs的深度学习 (DL) 可以准确地分类纹理. 这一新框架为图像分析提供了一种简单的方法,在预后信息提取方面可能超过人类观察者.

关键词:
卷积神经网络是一种卷积神经网络.有效的网络-B7图像的分类图像的分类.组织组织组织组织.在VGG-16中.

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科学领域:

  • 计算机科学 计算机科学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 深度学习 (DL) 已成为纹理分类和组织定位的领先方法,超过了传统的机器学习.
  • 卷积神经网络 (CNN) 是DL的关键组成部分,可实现高级图像分析.
  • 转移学习通过利用预先训练的网络来提高DL模型的性能.

研究的目的:

  • 引入用于图像级纹理分类的新数据集.
  • 评估基于集成转移学习的EfficientNet-B7 CNN对纹理分类的性能.
  • 评估框架的简单性和提取预后信息的潜力.

主要方法:

  • 为培训,验证和测试开发一个包含381张图像 (150x150像素) 的数据集.
  • 实施一个EfficientNet-B7深层卷积神经网络 (CNN) 模型.
  • 整合转移学习技术以提高分类准确性.

主要成果:

  • EfficientNet-B7模型在培训数据集上实现了高准确度 (89.33%).
  • 验证和测试准确率分别为52.43%和51.326%.
  • 培训损失记录为培训的0.2513,验证的1.846和测试的1.6137.

结论:

  • 使用EfficientNet-B7的拟议DL框架证明了对纹理分类的有效性.
  • 该研究强调了框架的设置和执行的直接性.
  • 该技术显示出与人类分析相比,可以提取更多预后信息的潜力.