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Swin-GA-RF:基于遗传算法的Swin转换器和随机森林,用于增强子宫癌分类

Manal Abdullah Alohali¹, Nora El-Rashidy², Saad Alaklabi³

¹Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Frontiers in oncology

|August 5, 2024

概括

这项研究介绍了Swin-GA-RF,这是一种通过巴氏涂片图像增强宫癌检测的新方法. Swin-GA-RF显著提高了分类准确性,为早期诊断提供了一个有前途的工具.

科学领域:

医疗成像医学成像
计算生物学计算生物学
人工智能的人工智能

背景情况:

宫癌是一个日益严重的全球健康问题,需要在早期检测方法方面取得进展.
计算机视觉,特别是Swin变压器,在医学图像分析中显示出潜力,优于传统方法.
在巴氏涂片图像中精确分类宫细胞对于及时干预至关重要.

研究的目的:

开发和评估一种新的方法,Swin-GA-RF,用于改善宫癌细胞分类.
为了利用Swin变压器的能力与遗传算法特征选择和随机森林分类相结合.
使用SIPaKMeD1数据集,提高宫癌查的性能.

主要方法:

使用Swin变压器从宫细胞图像中提取特征.
采用遗传算法 (GA) 进行最佳特征选择.
集成了一个随机森林分类器,取代了标准的softmax层,并应用了数据增强.

主要成果:

与其他斯温变压器和预训练的CNN模型相比,Swin-GA-RF表现出了卓越的性能.
在两个分类任务中都取得了高精度 (99.012%二进制,98.808%五类) 和F1分数.
亚当优化器为Swin-GA-RF模型提供了最好的结果.

结论:

拟议的Swin-GA-RF方法对于从巴氏涂片图像进行宫癌分类是非常有效的.
这种方法显示出提高早期宫癌诊断的准确性和效率的巨大潜力.
斯温-GA-RF为宫癌查计划和妇女健康倡议提供了宝贵的进步.

关键词:

美国有线电视新闻 (CNN) 模特儿斯温变压器变压器遗传算法是一种遗传算法.图像癌症的分类图像癌症的分类图像的分类图像的分类.图像处理是图像处理的过程.随机的森林随机的森林

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