一个基于人工智能的白血病自动分类系统,利用维度阿基米德斯优化
1Department Communication and Electronics Engineering, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt. warda_mohammed@nilehi.edu.eg.
Scientific reports
|May 16, 2025
概括
一个新的人工智能 (AI) 系统,白血病分类系统 (LCS),通过分析血液细胞图像来准确检测白血病. 这种AI系统有助于早期诊断,改善患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 血液学 血液学 血液学
背景情况:
- 白血病是一种普遍存在的血液癌症,其特点是不受控制的白细胞增殖.
- 这种增殖会损害骨髓功能,影响血小板和红细胞的产生,并可能损害器官.
- 早期发现和分类白血病对于有效治疗和患者的生存至关重要.
研究的目的:
- 提出一种新的人工智能 (AI) 系统,用于准确和早期的白血病检测和分类.
- 开发一个强大的多阶段系统,集成图像处理,细分,特征提取和分类.
主要方法:
- 白血病分类系统 (LCS) 采用五个阶段的管道:图像处理 (IPS),图像分割 (ISS),特征提取 (FES),特征选择 (FSS) 和分类 (CS).
- 用维度阿基米德优化算法 (DAOA) 来进行特征选择,提取和改进纹理和形态特征.
- 纳入维度学习策略 (DLS) 的DAOA提高了融合精度和效率.
主要成果:
- 拟议的LCS在白血病分类中与现有方法相比,表现优越.
- 整合DAOA用于特征选择显著提高了分类过程的准确性和效率.
结论:
- 开发的基于AI的白血病分类系统 (LCS) 为早期和准确的白血病诊断提供了一个有前途的工具.
- 新的特征选择方法DAOA有效地识别了关键特征,提高了分类性能.
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