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从美国图像中提取的新混合特征用于乳腺癌分类

Gigi Tăbăcaru¹, Simona Moldovanu^2,3, Dan Munteanu⁴

¹Department of Automatic Control, Faculty of Automation, Computers, Electrical, Engineering and Electronics, "Dunărea de Jos" University of Galati, Galaţi, Romania.

Scientific reports

|July 16, 2025

概括

本研究引入了使用自动机器学习 (AutoML) 进行乳腺癌 (BC) 分类的新型混合特征 (HF). 这些HF结合了纹理和形状,实现了高精度,有助于区分良性和恶性瘤.

科学领域:

医学成像和人工智能医学成像和人工智能
计算病理学计算病理学
机器学习在瘤学中的应用

背景情况:

对良性和恶性乳腺癌 (BC) 的准确分类对于有效治疗至关重要.
人工智能 (AI) 和图像处理为提高BC诊断提供了有希望的工具.
现有方法可以从新型特征提取和自动分类方法中获益.

研究的目的:

开发和评估用于乳腺癌分类的新杂交特征 (HF).
为了比较两个自动机器学习 (AutoML) 工具的性能,PyCaret和TPOT,在使用HF对BC进行分类.
确定最佳的分类器和特征,以区分良性乳腺癌和恶性乳腺癌.

主要方法:

计算原始混合特征 (HF) 通过整合Haralick纹理特征和Hu时刻通过多项式回归.
使用两个AutoML平台对BC进行分类:PyCaret和TPOT (基于树的管道优化工具).
使用集成机器学习 (EML) 技术验证结果,包括包装,提升和堆叠.

主要成果:

PyCaret使用AdaBoost分类器 (ADB) 选择了HF (和Hu时刻),达到91.4%的准确性.
TPOT使用了多层感知子分类器与HF,获得了90.6%的准确性.
拟议的HF有效地捕获了病变纹理和形状特征,与医生诊断相比较.

结论:

新的混合特征表明,对于准确的乳腺癌分类有很大的潜力.
像PyCaret和TPOT这样的AutoML工具可以有效地识别医疗图像分析的最佳模型.
开发的方法提高了计算效率,并减少了BC诊断中的过拟合风险.

关键词:

在AutoML中使用AutoML.整合机器学习机器学习哈拉利克的特点是哈拉利克的特点.这一刻的胡胡.混合动力车的特点是混合动力车的特性.

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