使用GWO增强视力变压器进行扩大独立乳腺癌诊断,具有多阶段斑点正常化功能
Taiyaba Fatma1, Prabhat Kumar Sahu2, Sasanka Choudhury3
1Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India.
Scientific reports
|March 4, 2026
概括
这项研究开发了一种自动化乳腺癌诊断系统,使用了组织病理学图像. 灰狼优化器视觉转换器 (GWO-ViT) 框架实现了高精度,为临床使用提供了可靠的工具.
科学领域:
- 数字病理学数字病理学
- 计算生物学是一种计算生物学.
- 医学成像分析分析 医学成像分析
背景情况:
- 从组织病理学图像准确诊断乳腺癌是具有挑战性的,因为染色和放大变化的变化.
- 需要自动化分类系统来提高诊断效率和一致性.
研究的目的:
- 开发和评估一个自动化乳腺癌诊断自动化乳腺癌诊断的综合框架.
- 通过使用先进的预处理和增强技术,应对着色变性和类不平衡的挑战.
- 用灰狼优化器 (GWO) 优化视觉变压器 (ViT) 模型,以在不同放大度上进行强大的分类.
主要方法:
- 在BreakHis数据集中应用了四个阶段的染色正常化管道 (CLAHE,直方图匹配,灰色的阴影,Macenko).
- 使用特定放大的数据增强来平衡类和增加数据集大小.
- 一个视觉转换器 (ViT) 模型,由灰狼优化器 (GWO) 优化,用于每个放大 (40X,100X,200X,400X).
主要成果:
- 拟议的GWO-ViT框架实现了高分类准确率:92.1% (40X),92.9% (100X),93.5% (200X) 和94.0% (400X).
- 交叉验证显示出最小的差异,表明模型性能和概括能力强大.
- 该框架被证明是计算效率高和变化不变的.
结论:
- GWO-ViT框架为自动化乳腺癌组织病理学分类提供了可靠和高效的解决方案.
- 综合方法有效地处理着着色差异和阶级不平衡.
- 开发的系统显示了将其整合到临床诊断工作流程中的巨大潜力.
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