经典和深度学习特征与增量学习的融合,以改进肺癌和结肠癌的分类
Mullakuri Anusha1, D Srinivasulu Reddy2
1Department of ECE, Jawaharlal Nehru Technological University Anantapur, Anantapuramu, India. mullakurianusha@gmail.com.
Scientific reports
|November 19, 2025
概括
这项研究提出了一种混合深度学习模型,用于准确的肺癌和结肠癌组织病理图像分类. 这种新的方法实现了高精度,证明了其临床适用性和改善癌症诊断的潜力.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 用于医学成像的深度学习
背景情况:
- 对肺癌和结肠癌的精确的组织病理图像分类对于临床病理学至关重要.
- 现有的方法在有效分析复杂的遗传病理特征方面面临挑战.
研究的目的:
- 开发一个混合深度学习网络,以进行强大的多类异位病理图像分类.
- 提高医疗图像分析的深度学习模型中的适应性和减少灾难性遗忘.
主要方法:
- 一个混合网络,将手工制作的功能 (LBP,GLCM,波形,色彩,形态) 与EfficientNetB0.0的深度功能相结合.
- 使用基于变压器的注意力融合策略来实现异质表示集成.
- 采用适应式增量学习方法,以阶段智能的数据增强.
主要成果:
- 该模型实现了高精度:在LC25000上达到99.87%,在NCT-CRC-HE-100K上达到99.07%,在HMU-GC-HE-30K上达到98.4%.
- 在多个独立数据集中展示了一致的性能.
- 验证了框架的通用性,可扩展性和临床适用性.
结论:
- 拟议的混合深度学习框架为多类病理学图像分类提供了强大的解决方案.
- 该模型的性能突显了其在帮助病理学家诊断癌症方面的潜力.
- 公共可用的代码和数据有助于可复制性和计算病理学的进一步研究.
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