PEAO:一个生物灵感的并行优化器,具有用于乳腺癌诊断的多策略通信机制
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, 50603, Malaysia.
Scientific reports
|January 13, 2026
概括
一个新的算法,即并行酶动作优化器 (PEAO),可以提高乳腺癌的诊断. PEAO优化了BiLSTM网络,显著提高了早期检测和患者生存的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 乳腺癌是全球主要的健康问题,需要改进的诊断工具.
- 准确和早期的乳腺癌分类对于有效的治疗和生存率至关重要.
研究的目的:
- 介绍一个新的元启发算法,即并行酶动作优化器 (PEAO).
- 应用PEAO来优化乳腺癌诊断BiLSTM网络的超参数.
主要方法:
- 开发了并行酶动作优化器 (PEAO),具有并行策略,多策略通信和莱维飞行.
- 集成PEAO与BiLSTM网络进行乳腺癌分类.
- 在威斯康星州乳腺癌诊断 (BCWD) 和威斯康星州乳腺癌 (WBC) 数据集上验证了PEAO-BiLSTM模型.
主要成果:
- PEAO-BiLSTM模型在分类准确性,精度,回忆和F1分数方面取得了显著的改进.
- 性能优于最近的元启发算法和其他超参数优化方法.
- 在BCWD和WBC数据集上验证了强大的性能.
结论:
- PEAO-BiLSTM方法为乳腺癌诊断提供了一个可靠的框架.
- 为开发智能,数据驱动的临床决策支持系统做出贡献.
- 强调PEAO在提高医学诊断准确性的潜力.
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