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基于机器学习的肺癌诊断研究

Haihui Huang¹, Aitong Zhong¹, Decheng Miao¹

¹Provincial Demonstration Software Institute, Shaoguan University, Shaoguan, China.

Technology and health care : official journal of the European Society for Engineering and Medicine

|July 18, 2025

概括

机器学习模型有效地将肺组织分类为良性或恶性,并评估癌症的攻击性. 混合L1/2 + L2规范化 (HLR) 达到96.67%的准确率,而人工神经网络 (ANN) 达到91.82%的准确率.

科学领域:

计算生物学是一种计算生物学.
医疗信息学医学信息学
机器学习在瘤学中

背景情况:

准确的瘤分类对于有效的癌症治疗至关重要.
区分良性瘤和恶性瘤是一个重大的诊断挑战.
机器学习为改善诊断准确性和速度提供了潜在的解决方案.

研究的目的:

评估机器学习方法来将肺组织分类为良性或恶性.
评估各种算法的性能,以确定肺癌的攻击性.
为了比较用于肺癌诊断的内置特征选择和没有内置特征选择的方法.

主要方法:

采用人工神经网络 (ANN) 和物流回归变体.
在没有内置特征选择的情况下使用的方法:ANN,后勤回归,峰处罚后勤回归.
使用内置特征选择的应用方法:拉索惩罚后勤回归,弹性网惩罚后勤回归和混合L1/2 + L2规范化 (HLR).

主要成果:

在分类良性/恶性肺组织 (没有特征选择) 中,ANN获得了91.82%的准确性.
在分类良性/恶性肺组织 (具有特征选择) 中,HLR实现了96.67%的准确性.
在评估肺癌的攻击性 (没有特征选择) 中,ANN的准确率达到了84.74%.
在评估肺癌的攻击性 (具有特征选择) 中,HLR达到93.33%的准确性.

结论:

在整合特征选择时,HLR在分类肺组织和评估癌症攻击性方面表现出卓越的表现.
在没有内置特征选择的方法中,ANN在两种分类任务中都表现出强大的竞争力.
无论是HLR还是ANN,都显示出改善肺癌诊断和分级的巨大潜力.

关键词:

癌症的诊断癌症的诊断这是分类分类的分类.功能选择功能选择肺癌是一种肺癌.机器学习是机器学习.

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