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BCD-TransNet:使用转移学习方法自动检测和分类乳腺癌.

Amanullakhan M1, Sridhar P2, Indra J3

  • 1Department of Electronics and Communication Engineering, Mohamed Sathak Engineering College, Kilakarai, India.

Technology and health care : official journal of the European Society for Engineering and Medicine
|May 7, 2025
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概括

一个新的转移学习框架,BCD-TransNet,从基因病理图像准确地分类乳腺癌 (BC) 阶段. 这种方法通过提高对现有技术的分类性能来增强早期诊断.

关键词:
贪的蛇优化 贪的蛇优化克里尔群优化算法 克里尔群优化算法乳腺癌 乳腺癌 乳腺癌决策树是一个决策树.基于静止波形波段的Retinex可以使用.

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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 乳腺癌 (BC) 是女性的主要死亡原因,早期诊断对于有效治疗至关重要.
  • 目前的机器学习 (ML) 技术在准确分类BC和帮助早期诊断方面面临着挑战.
  • 转移学习显示了使用基因病理图像改进BC分类的前景.

研究的目的:

  • 通过转移学习网络 (BCD-TransNet) 引入一种新的乳腺癌检测方法,用于识别和分类BC阶段.
  • 通过先进的转移学习框架,提高BC诊断的准确性和效率.
  • 克服BC分类和早期检测现有的ML技术的局限性.

主要方法:

  • 来自BreakHis数据集的组织病理图像使用基于静止波段的Retinex (SWR) 进行了预处理,以减少噪声和提高质量.
  • 图像细分使用混合贪的蛇-群优化 (HGS-KHO) 算法进行.
  • BCD-TransNet模型使用了五个预训练的网络来提取特征,然后是两级分类和基于ML的决策树来进行分阶段.

主要成果:

  • BCD-TransNet模型在分类乳腺瘤方面取得了99.31%的高精度.
  • 与DLA-EABA,Pa-DBN-BC和TTCNN相比,提议的转移学习模型显示出更高的性能,精度分别提高了2.11%,13.31%和1.82%.
  • 该模型有效地进行了两级分类,区分良性和恶性细胞及其亚型.

结论:

  • BCD-TransNet模型在准确的乳腺癌分类和分期方面取得了重大进展.
  • 这种基于转移学习的方法为改善早期乳腺癌诊断提供了强有力的解决方案.
  • 拟议的方法表现出卓越的性能,突出了其在乳腺癌检测中临床应用的潜力.