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基于混合特征学习的PSO-PCA特征工程方法用于血液癌症分类

Ghada Atteia¹, Rana Alnashwan¹, Malak Hassan²

¹Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|August 26, 2023

概括

一种新的混合深度学习方法结合了主要组件分析 (PCA) 和粒子群优化 (PSO) 以改善急性淋巴细胞白血病 (ALL) 检测. 这种方法提高了血液外周图像 (BPIs) 的分类准确性,在贝叶斯优化的SVM中达到97.4%.

科学领域:

计算生物学和生物信息学
医学成像和诊断医学成像和诊断
机器学习和人工智能的人工智能

背景情况:

急性淋巴细胞白血病 (ALL) 是一种关键的血液癌症,需要早期检测才能有效治疗.
目前手动查血液涂抹图像的ALL是劳动密集型和容易出错.
深度学习计算机视觉系统显示出对ALL检测的承诺,但可能会遭受特征冗余和维度问题.

研究的目的:

开发一种先进的特征工程方法,用于在血液外周图像 (BPIs) 中增强ALL检测.
集成主要组件分析 (PCA) 和粒子集群优化 (PSO) 进行最佳特征选择.
提高ALL诊断系统的分类准确性和效率.

主要方法:

图像特征是使用谷歌网 (预训练的卷积神经网络-CNN) 提取的.
应用了主要组件分析 (PCA) 以保持95%的数据变化,并使用粒子群优化 (PSO) 进行最佳特征搜索.
创建了一个结合PCA和PSO输出的混合功能集,用于训练贝叶斯优化的支持矢量机 (SVM) 和子空间歧视集体学习 (SDEL) 分类器.

主要成果:

与单个PCA,PSO或原始提取特征相比,拟议的混合特征集显著提高了分类性能.
用混合PCA-PSO功能集训练的贝叶斯优化SVM分类器实现了97.4%的高分类精度.
开发的特征工程方法证明了与所有多类分类的当前最先进方法相比具有竞争力的性能.

结论:

混合PCA-PSO功能工程方法有效地解决了ALL检测的深度学习中的维度挑战.
这种新的方法提高了机器学习分类器在诊断BPI的ALL时的准确性和效率.
该研究强调了整合缩小维度和进化计算的潜力,以进行强大的医疗图像分析.

关键词:

这是分类分类的分类.深度学习是一种深度学习.功能选择功能选择这种白血病是白血病.机器学习是机器学习.优化的优化优化优化.

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