用粗略的集合理论和竞争性搜索算法的修订版本来诊断口腔癌的最佳分层方法
Simin Song1, Xiaojing Ren2, Jing He1
1The Second Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100089, China.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了使用优化特征选择和支持矢量机 (SVM) 分类来进行口腔癌图像诊断的高效管道. 与现有技术相比,新方法在识别口腔癌病例方面表现优越.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算生物学 计算生物学
背景情况:
- 口腔癌以不受控制的细胞生长为特征,如果不及早检测到,就会造成严重的健康风险.
- 及时诊断口腔癌对于有效治疗和改善患者结果至关重要.
- 目前用于口腔癌图像的诊断方法可以通过先进的计算方法来改进.
研究的目的:
- 通过图像提出一种新的,高效的管道来诊断口腔癌.
- 提高口腔癌检测系统的准确性和效率.
- 优化特征选择和分类,以提高诊断性能.
主要方法:
- 图像预处理和细分以隔离感兴趣的区域.
- 使用修改后的竞争性搜索优化器进行特征提取和优化选择.
- 使用支持矢量机器 (SVM) 进行口腔癌图像的分类.
主要成果:
- 拟议的管道实现了口腔癌图像的有效诊断.
- 该方法与权重平衡,SVM,GLCM,深度学习,转移学习,移动显微镜和二次差异分析相比,表现出更高的性能.
- 使用四个指标的验证证实了该方法的有效性.
结论:
- 开发的管道为口腔癌图像诊断提供了一种高效和有效的方法.
- 优化的特征选择和SVM分类显著提高了诊断准确度.
- 这种技术对早期检测和治疗口腔癌具有前途.
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