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Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...

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通过增强的卷积序列网络改进肺癌检测

Usman Haziq1, Jamal Uddin1, Shahid Rahman2

  • 1Department of Computer Science, Riphah International University, Lahore, 55150, Punjab, Pakistan.

Scientific reports
|September 1, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了用于肺癌检测的优化顺序折叠网络 (SCNN). 该SCNN模型显著提高了分类准确性,并减少了组织学图像的处理时间.

关键词:
卷积神经网络卷积序列网络深度学习组织学数据集肺癌 肺癌

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科学领域:

  • 癌症学
  • 医学成像
  • 人工智能

背景情况:

  • 肺癌是全球癌症死亡的主要原因,
  • 目前用于肺癌诊断的医学成像技术面临诸如假阳性和假阴性等局限性.
  • 传统的深度学习模型,如卷积神经网络 (CNN),表现出高度的计算复杂性和缓慢的推断.

研究的目的:

  • 开发一个优化的序列折叠网络 (SCNN),以准确有效地分类肺癌.
  • 在速度和计算负载方面解决传统CNN的局限性.
  • 提高深度学习在临床肺癌诊断中的实用性.

主要方法:

  • 拟议的SCNN模型包括三个可折叠层,三个最大可聚合层,平面层和密集层.
  • 该模型在含有腺癌,良性和状细胞癌的组织成像数据集上进行了训练和评估.
  • 与传统的CNN,R-CNN和自定义开始分类器进行了比较.

主要成果:

  • 在60个时代内,SCNN模型的平均精度为95.34%和F1得分.
  • 分类准确率达到95.66%,回忆率为95.33%.
  • 与传统的CNN方法相比,SCNN表现出更高的速度和稳定性,在1000秒内完成分类.

结论:

  • SCNN为提高肺癌检测准确性和效率提供了一种实用且可扩展的解决方案.
  • 这种优化的深度学习方法比组织学图像分类的现有方法有了显著的进步.
  • 该SCNN的表现表明其有可能提高肺癌诊断的临床实践和患者结果.