在宫癌中进行状细胞分类的深度合体学习方法
Jayesh Gangrade1, Rajit Kuthiala1, Shweta Gangrade2
1Department of Artificial Intelligence & Machine Learning, School of Computer Science & Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India.
Scientific reports
|March 2, 2025
概括
这项研究引入了一种先进的计算机辅助方法来分类宫状状细胞,这对于早期宫癌检测至关重要. 结合CNN,AlexNet和SqueezeNet的组合技术实现了94%的准确性,为资源有限的设置提供了一个有希望的工具.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 宫癌是全球重要的健康问题,而巴氏涂片分析是关键的诊断工具.
- 目前的巴氏涂片分析是劳动密集型和耗时的,特别是在资源有限的环境中.
- 需要使用高效的计算机辅助方法进行子宫癌预分析.
研究的目的:
- 开发和评估一套机器学习技术,用于分类宫状状细胞.
- 评估个别模型 (CNN,AlexNet,SqueezeNet) 和拟议的整体方法的准确性.
- 为宫癌提供更有效的诊断工具,特别是在资源有限的环境中.
主要方法:
- 利用了来自SimpakMed (Kaggle) 的4096多张宫细胞图像的数据集.
- 采用集体技术,集成卷积神经网络 (CNN),AlexNet和SqueezeNet进行图像分类.
- 将状细胞分为五个不同的类别,以评估宫癌的严重程度.
主要成果:
- 单个模型的准确度达到90.8% (CNN),92% (AlexNet) 和91% (SqueezeNet).
- 拟议的整体技术显著优于单个模型,达到94%的准确性.
- 整体方法在精确的状细胞分类中表现出高效率.
结论:
- 开发的集体技术为状细胞分类提供了一个高度准确和高效的方法.
- 这种方法显示出在资源有限的环境中改善宫癌诊断的重大前景.
- 这项研究强调了先进的计算方法在增强子宫癌查计划中的潜力.
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