对FOX优化算法的二进制变体进行比较性能分析,使用半方格组合排名方法来检测甲状腺癌
Rohit Sharma1, Gautam Kumar Mahanti1, Ganapati Panda2
1Department of Electronics and Communication Engineering, NIT, Durgapur, 713209, India.
Scientific reports
|November 10, 2023
概括
这项研究引入了一种新的机器学习方法来检测甲状腺癌,使用二进制FOX优化算法进行特征选择. 拟议的模型在超声波和组织病理学数据集上实现了高精度和效率.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 机器学习 机器学习
- 内分泌系统瘤学 内分泌系统瘤学
背景情况:
- 甲状腺癌虽然比其他癌症少见,但对内分泌系统有很大影响.
- 机器学习 (ML) 为诊断甲状腺异常提供了先进的工具.
- 在机器学习中,有效的特征选择对于减少数据维度,提高模型性能和提高可解释性至关重要.
研究的目的:
- 调查FOX优化算法的二进制变体,以在甲状腺癌检测中有效选择特征.
- 通过基于视觉变压器的特征提取和天真贝叶斯分类来评估这些算法的性能.
- 为了优化多目标任务,最大限度地提高准确性,F2得分,AUC-ROC,并最大限度地减少特征空间大小.
主要方法:
- 采用了八个传输函数来创建二进制FOX优化算法.
- 利用DeiT和Swin变压器模型从超声波和状腺癌图像中提取特征.
- 应用了局部线性嵌入来进行特征转换,然后使用二进制FOX优化来选择特征,并使用一个纯粹的贝叶斯分类器.
主要成果:
- 带有V1转移函数的二进制FOX优化算法与Swin变压器和Naïve Bayes相结合,表现出卓越的性能.
- 在超声波数据集上获得了94.75%的准确性,0.9848 AUC-ROC和0.9365 F2-Score,只选择了5个特征.
- 对于基因病理学数据集,该模型使用12个特征达到89.71%的准确性,0.9329 AUC-ROC和0.8760 F2-Score.
结论:
- 拟议的框架有效地使用减少的特征空间识别甲状腺癌,与现有研究相比.
- 优化的二进制FOX优化算法为甲状腺癌诊断提供了高度准确和高效的方法.
- 这种方法通过先进的机器学习技术提高了内分泌系统癌症的诊断能力.
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