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增强的超像素引导ResNet框架与优化深度加权平均化基于特征融合,用于在组织病理图像中检测肺癌.

Karthikeyan Shanmugam1, Harikumar Rajaguru1

  • 1Bannari Amman Institute of Technology, Tamil Nadu 638401, India.

Diagnostics (Basel, Switzerland)
|April 12, 2025
PubMed
概括

这项研究引入了用于肺癌分类的自动深度学习方法,达到98.68%的准确性. 该方法使用特征融合和优化来提高诊断效率和准确性,这对于早期肺癌检测至关重要.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 肺癌是导致死亡的主要原因,需要早期诊断以改善生存率.
  • 手动组织病理学分析用于肺癌诊断是一个耗时的过程.
  • 深度学习有可能提高肺癌诊断的准确性和效率.

研究的目的:

  • 开发和评估基于深度学习的方法论,用于自动化肺癌分类.
  • 通过先进的特征提取,融合和优化技术来提高诊断准确性和效率.
  • 为了比较不同深度学习架构和优化算法在肺癌分类中的性能.

主要方法:

  • 使用自适应模糊过器进行图像预处理,并通过修改的简单线性代集群 (SLIC) 算法进行细分.
  • 使用ResNet-50,ResNet-101和ResNet-152 (RN-X) 深度学习架构进行特征提取.
  • 使用基于深度加权平均的特征融合 (DWAFF) 技术进行特征融合,然后通过粒子群优化 (PSO) 和红鹿优化 (RDO) 进行优化.
  • 使用各种机器学习模型进行分类,包括支持向量机 (SVM),决策树 (DT),随机森林 (RF),K-最近邻居 (KNN),SoftMax歧视分类器 (SDC),贝叶斯线性歧视分析分类器 (BLDC) 和多层感知子 (MLP),通过K折交叉验证进行评估.

主要成果:

关键词:
在MLP中,MLP是MLP.公共服务人员 (PSO)在RDO RDO的基础上.RN-XX 在美国在SLIC细分的细分.进行交叉验证.

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  • 拟议的DWAFF技术与RDO特征选择和MLP分类相结合,在K=10交叉验证下实现了最高的分类准确率98.68%.
  • 在ResNet-X (RN-X) 融合的特性中,其性能优于单个ResNet变体.
  • 整合图像细分和功能优化显著提高了分类准确性.

    • 结论:

    • 开发的方法自动化肺癌分类有效使用深度学习,功能融合和优化.
    • 图像细分和特征选择对于提高诊断性能和肺癌分类准确性至关重要.
    • 未来的研究可能将重点放在进一步优化策略和肺癌诊断的混合深度学习模型的开发上.