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可转移的深度学习与基于coati优化算法的线粒细胞核细分和分类模型

Amal Alshardan¹, Nazir Ahmad², Achraf Ben Miled³

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

Scientific reports

|December 20, 2024

概括

这项研究引入了一种新的深度学习技术,用于在组织病理图像中准确识别线粒细胞核 (MN),改善癌症分级. COADL-MNSC方法在细分和分类这些关键癌症标志物方面取得了高准确性.

科学领域:

医疗图像分析医学图像分析
计算病理学计算病理学
人工智能在瘤学中的应用

背景情况:

精确的线粒细胞计数 (MC) 对于癌症分类至关重要,传统上由病理学家手动进行.
在基因病理图像 (HIs) 中手动MC评估是耗时且容易变化的.
为了提高诊断准确性,需要自动化的线粒细胞核 (MN) 分段和HI分类方法.

研究的目的:

提出一种新的技术,即Coati优化算法与深度学习驱动的线粒细胞核细分和分类 (COADL-MNSC),用于在HI中自动检测MN.
通过改善MN的细分和分类来提高癌症分级的准确性和效率.
利用深度学习模型进行精确的特征提取和MN的分类.

主要方法:

使用中位过 (MF) 进行HI的预处理.
使用混合注意力融合U-Net (HAU-UNet) 模型对MN进行细分.
使用一个由Coati优化算法 (COA) 优化的囊网络 (CapsNet) 进行特征提取.
使用双向长期短期记忆 (BiLSTM) 模型对MN进行分类.

主要成果:

COADL-MNSC方法在一个MN图像数据集上表现出色.
与现有技术相比,在各种指标上实现了98.89%的卓越准确性.
综合方法有效地细分和分类了线粒细胞核.

结论:

拟议的COADL-MNSC技术提供了一个高度准确和高效的自动化解决方案,用于在组织病理图像中检测线粒细胞核.
这种基于深度学习的方法有可能在癌症分类和诊断方面显著帮助病理学家.
该研究强调了将优化算法与深度学习架构相结合,用于复杂的医学图像分析任务的有效性.

关键词:

囊网络是一个囊网络.科蒂优化算法科蒂优化算法这就是HAU-UNet.过的中位数过.线粒子核中的线粒子核

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