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Updated: Jun 13, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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用于灰度医学图像分类的像素嵌入.

Wensu Liu1,2, Na Lv1,2, Jing Wan1,2

  • 1Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, Liaoning, 110122, China.

Heliyon
|September 10, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的灰度医学图像分类方法,通过将n-gram特征与像素平整相结合. 该方法有效地保存空间信息,在多个基准数据集中实现高精度.

关键词:
分类 分类 分类 分类.医疗图像的灰度尺度为灰度尺度.像素 像素 像素 像素 像素文本嵌入式 文本嵌入式

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 准确分类灰度医学图像对于诊断至关重要.
  • 现有的文本嵌入架构需要适应图像数据.
  • 保存空间信息是医学图像特征提取的一个关键挑战.

研究的目的:

  • 扩展文本嵌入架构,以实现有效的灰度医学图像分类.
  • 开发一种方法,在特征表示过程中保存空间信息.
  • 评估不同医学图像数据集的拟议方法.

主要方法:

  • 一种新的机制,将n-gram特征与像素平整相结合.
  • 使用列顺序,行顺序,对角顺序和反对角顺序的像素排序.
  • 实施性能评估的十倍交叉验证.

主要成果:

  • 在医学MNIST上获得了99.92%的准确性.
  • 在胸部X射线肺炎 (90.06%),Curated Covid CT (96.94%) 和超声波 (93.17%) 数据集上表现出高性能.
  • 在MIAS数据集上获得了79.11%的准确性,这表明在不同复杂性的数据中性能强.

结论:

  • 拟议的方法有效地扩展了用于医学图像分类的文本嵌入.
  • 像素平面化技术成功地保存了关键的空间信息.
  • 该方法显示了改善医学成像诊断准确性的巨大潜力.