摘要
临床使用疑似溶血血浆易引发体外溶血症,其症状包括心衰、严重贫血等。将深度学习方法应用于血浆图像能显著提高识别精度,因此本文提出一种基于改进型“你只看一次”系列网络第5代版本(YOLOv5)的血浆品质检测模型。然后,在血浆数据集上引入本文模型和评价体系,最终分类识别的平均精度均值达到98.7%。本文实验结果表明,通过算法网络中的全维动态卷积、分离式核注意力池化、残差双向信息融合以及重参数化模块组合,能高效获取空间映射特征信息,提高血浆品质检测的平均识别精确度。综上,本文方法可以实现对血浆图像的高效检测,为预防体外溶血症提供了一种具有应用价值的检测方法。
In the clinical stage,suspected hemolytic plasma may cause hemolysis illness,manifesting as symptoms such as heart failure,severe anemia,etc.Applying a deep learning method to plasma images significantly improves recognition accuracy,so that this paper proposes a plasma quality detection model based on improved“You Only Look Once”5th version(YOLOv5).Then the model presented in this paper and the evaluation system were introduced into the plasma datasets,and the average accuracy of the final classification reached 98.7%.The results of this paper's experiment were obtained through the combination of several key algorithm modules including omni-dimensional dynamic convolution,pooling with separable kernel attention,residual bi-fusion feature pyramid network,and re-parameterization convolution.The method of this paper obtains the feature information of spatial mapping efficiently,and enhances the average recognition accuracy of plasma quality detection.This paper presents a high-efficiency detection method for plasma images,aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.
作者
张瀚文
孙渝
江浩
胡金田
罗刚银
李栋
曹维娟
邱香
ZHANG Hanwen;SUN Yu;JIANG Hao;HU Jintian;LUO Gangyin;LI Dong;CAO Weijuan;QIU Xiang(Engineering Laboratory of Advanced In Vitro Diagnostic Technology Chinese Academy of Sciences,Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Science,Suzhou,Jiangsu 215163,P.R.China;College of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,P.R.China;Suzhou Blood Center,Suzhou,Jiangsu 215006,P.R.China)
出处
《生物医学工程学杂志》
北大核心
2025年第1期123-131,139,共10页
Journal of Biomedical Engineering
基金
中国科学院先导A类专项课题(XDA16021100)
中国科学院科研仪器设备研制项目(ZDZBGCH2018003)
苏州市重大疾病、传染病预防和控制关键技术(研究)项目(20210419115032678,GWZX202102)
山东省自然科学基金(ZR2023QF169)。
关键词
全维动态卷积
分离式核注意力池化
残差双向融合特征金字塔网络
连续重参数化卷积
Omni-dimensional dynamic convolution
Pooling separable kernel attention
Residual bifusion feature pyramid network
Re-parameterization convolution
