转移学习与亚当金 Rush 优化子宫内膜疾病分类使用组织病理图像的优化

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Sudhagar Dhandapani¹, Ravikumar Subburam², Pretty Diana Cyril Cyriloose³

¹Department of Information Technology, Jerusalem College of Engineering, Chennai, India.

Microscopy research and technique

|July 9, 2025

概括

一个新的转移学习卷积神经网络与Adam Gold Rush优化 (TL-CNN_AdGRO) 准确地从组织病理图像中分类子宫内膜癌,改善早期检测和患者存活率.

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

子宫内膜癌是一种影响女性生殖器官的重大疾病,需要早期和准确的诊断以改善存活率.
目前对子宫内膜癌的诊断方法可以通过应用在组织病理图像上的先进计算技术来增强.

研究的目的:

提出一种新的基于转移学习的卷积神经网络与Adam Gold Rush优化 (TL-CNN_AdGRO) 进行子宫内膜癌的分类.
评价提议的TL-CNN_AdGRO模型在通过基因病理图像准确识别子宫内膜癌的性能.

主要方法:

组织病理图像经过预处理,使用自适应加权平均波器 (AWMF).
子宫内膜癌细分是使用方向连接网络 (DConn-Net) 进行的.
特性提取包括本地边界总和模式 (LBSP) 和本地加伯二进制模式直方形序列特征 (LGBPHS),然后使用使用AdGRO算法训练的TL-CNN进行分类.

主要成果:

拟议的TL-CNN_AdGRO模型实现了91.876%的高精度.
卓越的性能指标包括一个真正正比率 (TPR) 为93.987%和一个真负比率 (TNR) 为89.876% (K样本8).
该模型与现有方法相比,显示出强度和有效性.

结论:

该TL-CNN_AdGRO模型显示,对于早期检测子宫内膜癌显著有前途.
这种方法为瘤学中的组织病理图像分析提供了一种强大而有效的方法.
这些发现支持先进的人工智能模型在改善癌症诊断方面的临床实用性.

关键词:

亚当黄金热优化优化亚当黄金热优化深度学习是一种深度学习.基于定向连接性的细分.这是子宫内膜癌的癌症.转移学习转移学习

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