基于改进型U-Net的变色油墨血浆判别模型

A plasma discrimination model for color changing ink based on improved U-Net

目的:为了解决因主观判别尺度不一和计算响应过长,在血浆制备过程中易出现疑似溶血血浆误判输出和疑似非溶血血浆医学报废的现象,给患者的生命安全带来极大隐患或产生浪费的问题。方法:研制一种基于深度学习和变色油墨理念的限界法。利用改进型U-Net网络进行图像分割,引入改进型注意力机制、批量归一化和填充模块来解决空间映射关系中存在的估计均值偏移、计算效率低和感受野视场不足的问题,并利用自采样本数据集对该模型进行验证对比。结果:采用变色油墨限界法进行分类判别,在确保血浆样本识别精度为前提的同时,提升了血浆判别的计算效率、降低了判别时间,实验结果评价准确率为99.52%。结论:本研究模型的血浆判别精度优于其他判别模型,有望应用于临床。

Objective:Due to inconsistent subjective assessment criteria and excessively long calculation responses,there is a high risk of mistakenly discarding suspected hemolytic plasma and inappropriately discarding suspected non-hemolytic plasma during plasma preparation,posing significant risks to patient safety and leading to waste.This study aims to resolue these problems.Methods:A thresholding method that integrates deep learning with color-changing ink concepts was developed.By employing an enhanced U-Net architecture for image segmentation,the study introduces an advanced attention mechanism,batch normalization,and a padding module to tackle issues such as mean estimation bias,computational inefficiencies,and limited receptive field sizes in spatial mapping relationships.The model was validated and compared using a self-collected sample dataset.Results:This study employed the color-changing ink boundary method for classification,enhancing the computational efficiency of plasma discrimination and reducing discrimination time,while ensuring the accuracy of plasma sample identification.The accuracy rate of the experimental results is99.52%.Conclusion:The results indicate that the plasma discrimination accuracy of this model is superior to other discrimination models,and it is expected to be applied in clinical practice.