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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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肺癌组织病理学图像分类使用转移学习与卷积神经网络模型.

Anandhavalli Muniasamy1, Salma Abdulaziz Saeed Alquhtani1, Syeda Meraj Bilfaqih1

  • 1College of Computer Science, King Khalid University, Abha, Saudi Arabia.

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概括

使用EfficientNetB7的深度学习准确地对肺癌 (LC) 组织病理学图像进行分类. 这种人工智能方法有助于早期诊断和治疗,改善患者的治疗结果.

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肺癌是一种肺癌.基因病理学图像 基因病理学图像卷积神经网络是一种卷积神经网络.有效的net7有效的net7图像网络 图像网络

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科学领域:

  • 计算病理学计算病理学
  • 人工智能在瘤学中的应用
  • 医疗图像分析 医学图像分析

背景情况:

  • 肺癌 (LC) 是一个严重的健康威胁,需要早期检测才能有效治疗.
  • 准确的组织学分类对于确定适当的LC管理策略至关重要.
  • 深度学习 (DL) 提供了在LC诊断中分析复杂的组织病理图像的潜力.

研究的目的:

  • 实施预训练的EfficientNetB7模型来对肺癌组织病理学图像进行分类.
  • 将LC分为主要恶性瘤类型:腺癌,状细胞癌和大细胞癌.
  • 用准确度作为主要指标来评估分类性能.

主要方法:

  • 利用了15,000张肺癌组织病理学图像的数据集.
  • 雇佣了EfficientNetB7,一个在ImageNet上预训练的卷积神经网络 (CNN),用于转移学习.
  • 在LC数据集上训练了EfficientNetB7模型并评估了其性能.

主要成果:

  • EfficientNetB7模型在分类LC组织病理图像方面实现了99.77%的高精度.
  • 这一性能超过或与之前研究报告的准确度水平相匹配 (90-99%).
  • 使用EfficientNetB7进行转移学习,有效地提取了相关特征以进行准确的分类.

结论:

  • 基于CNN的EfficientNetB7模型加速了从组织病理图像的肺癌诊断.
  • 这种人工智能工具可以减轻病理学家的工作量,促进早期的患者治疗.
  • 自动分类LC助力及时和精确的治疗干预.