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MSPO:一种机器学习超参数优化方法,用于增强乳腺癌图像分类.

Haonan Li¹, Vijay Govindarajan², Tan Fong Ang¹

¹Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayar Persekutuan, Malaysia.

|July 22, 2025

概括

本研究介绍了多策略优化器 (MSPO),用于改进乳腺癌图像分类. MSPO增强了深度学习模型,导致更准确的诊断和更好的患者结果.

关键词:

多策略优化器多策略优化器乳腺癌乳腺癌乳腺癌超参数优化超参数优化图像的分类图像的分类.机器学习是机器学习.

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科学领域:

医学成像医学成像
人工智能的人工智能是人工智能.
计算生物学是一种计算生物学.

背景情况:

乳腺癌是全球主要的健康威胁,需要早期诊断.
深度学习在乳腺癌图像分类方面表现有前途,但面临着超参数优化挑战.
传统的优化方法往往受到有限的有效性和过早的趋同的影响.

研究的目的:

提出和评估一个新的多策略优化器 (MSPO) 用于乳腺癌图像分类.
提高医疗图像分析中的深度学习模型的性能.
为了解决现有的超参数优化技术的局限性.

主要方法:

通过将Sobol序列初始化,非线性下降惯性重量和混乱参数集成到原来的Parrot Optimizer中,开发了MSPO.
验证了MSPO在CEC 2022基准函数上的表现,并对其变体进行了废除研究.
结合MSPO与ResNet18模型在BreaKHis数据集上进行乳腺癌图像分类.

主要成果:

与基准函数的领先算法相比,MSPO表现出更高的优化精度和融合率.
优化MSPO的ResNet18模型在BreaKH数据集上的非优化版本和替代优化算法表现明显优于MSPO.
废除研究证实了MSPO内部个别策略的有效性.

结论:

MSPO为优化任务提供了增强的全球勘探和趋同稳定性.
拟议的MSPO显示了医学图像分类的巨大潜力和实用价值,特别是在乳腺癌方面.
使用MSPO优化深度学习超参数可以提高乳腺癌检测中的诊断准确性和分类性能.