通过动态加权组合增强胰腺癌分类:一种游戏理论方法
Dhanasekaran S1, Silambarasan D2, Vivek Karthick P3
1Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.
Computer methods in biomechanics and biomedical engineering
|November 20, 2023
概括
这项研究介绍了一种基于游戏理论的新型组合框架,用于使用医学成像技术准确的胰腺癌分类. 与现有方法相比,这种方法显著提高了诊断性能.
科学领域:
- 医学成像分析 医学成像分析
- 计算瘤学计算瘤学
- 医疗保健中的人工智能
背景情况:
- 胰腺癌是癌症死亡的主要原因,由于其位于腹部,经常被诊断为晚期.
- 准确和早期分类胰腺癌对于有效治疗至关重要.
- 计算机辅助诊断 (CAD) 系统越来越多地用于用于癌症检测的放射性成像.
研究的目的:
- 开发和评估一个动态加权整体框架用于胰腺癌分类.
- 利用游戏理论来提高医学成像中的分类准确性.
- 整合转移学习和传统的机器学习方法,以进行可靠的诊断.
主要方法:
- 使用灰色级别共发生矩阵 (GLCM) 提取特征.
- 通过基于高斯核的模糊粗略集合理论 (GKFRST) 减少特征.
- 使用随机森林 (RF),ResNet50和VGG16与转移学习 (TL) 的分类.
- 使用游戏理论方法整合TL和RF结果的集成分类.
主要成果:
- 提出的基于游戏理论的集合分类器在胰腺癌分类中取得了显著更高的准确性.
- 与当前最先进的模型相比,该框架表现出了卓越的性能.
- 多种计算技术的整合导致了诊断能力的提高.
结论:
- 新的整体框架有效地提高了胰腺癌分类的准确性.
- 游戏理论为癌症分类中的战略相互作用提供了一个独特而强大的数学范式.
- 这项研究为计算瘤学领域提供了独特的方法论贡献.
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