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使用基于生物灵感的深度学习模型进行增强的超调,以准确检测和分类肺癌.

Jyoti Kumari1, Sapna Sinha1, Laxman Singh2

  • 1Department of Computer Science and Engineering, Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.

The International journal of artificial organs
|August 9, 2025
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概括

这项研究引入了一种增强超调深度学习 (EHTDL) 模型,用于准确检测肺癌 (LC). 这种新的方法显著提高了CT图像的早期诊断和分类效率.

关键词:
生物启发的算法生物启发的算法.早期诊断 早期诊断 早期诊断断形边缘分类器 断形边缘分类器灰狼优化优化 灰狼优化不同的进化是不同的进化.基于灰色水平共发生矩阵的纹理分析.面罩 R-CNN 面膜医学成像医学成像平滑的边缘增强增强.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 肺癌 (LC) 仍然是全球癌症死亡的主要原因,这强调了需要有效的早期检测方法.
  • 目前的LC检测技术受到计算复杂性,数据集成挑战,可扩展性问题和临床验证困难的阻碍.

研究的目的:

  • 开发一个增强的超调深度学习 (EHTDL) 模型,以提高肺癌检测和分类的准确性和效率.
  • 用生物灵感算法和先进的深度学习技术来解决现有方法的局限性.

主要方法:

  • 预处理CT图像使用光滑边缘增强 (SEE) 和基于GLCM的纹理分析来提取特征.
  • 采用混合特征选择方法与灰狼优化 (GWO) 和差异演变 (DE) 进行特征改进和维度降低.
  • 使用Mask R-CNN进行精确的肺部细分,并使用深层碎片边缘分类器 (DFEC) 与碎片块进行LC特征学习.

主要成果:

  • EHTDL模型实现了高性能指标:99%的准确性,100%的精度,98%的回忆力和99%的F1得分.
  • 在LC检测和分类方面证明了稳定性和有效性.
  • 由于其可扩展性和效率,它适合实时临床应用.

结论:

  • 拟议的EHTDL模型为早期肺癌检测提供了一个有希望的解决方案,大大提高了患者的护理.
  • 该模型的先进深度学习架构和优化技术克服了LC诊断中的现有挑战.
  • 这项研究为在抗击肺癌方面开发更有效,更准确的临床工具铺平了道路.