The differential of leukocytes functions as the first indicator in clinical examinations. However, microscopic examinations suffered from key limitations of low throughputs in classifying leukocytes while commercially available hematology analyzers failed to provide quantitative accuracies in leukocyte differentials. A home-developed imaging and impedance flow cytometry of microfluidics was used to capture fluorescent images and impedance variations of single cells traveling through constrictional microchannels. Convolutional and recurrent neural networks were adopted for data processing and feature extractions, which were then fused by a support vector machine to realize the four-part differential of leukocytes. The classification accuracies of the four-part leukocyte differential were quantified as 95.4% based on fluorescent images plus the convolutional neural network, 90.3% based on impedance variations plus the recurrent neural network, and 99.3% on the basis of fluorescent images, impedance variations, and deep neural networks. Based on single-cell fluorescent imaging and impedance variations coupled with deep neural networks, the four-part leukocyte differential can be realized with almost 100% accuracy.

中文翻译：

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基于微流体成像和阻抗流式细胞术与深度神经网络耦合的白细胞分类

白细胞分类是临床检查的首要指标。然而，显微镜检查在白细胞分类方面受到低通量的限制，而市售血液分析仪无法提供白细胞差异的定量准确性。使用自主开发的微流体成像和阻抗流式细胞术来捕获通过收缩微通道的单细胞的荧光图像和阻抗变化。采用卷积神经网络和循环神经网络进行数据处理和特征提取，然后通过支持向量机进行融合，实现白细胞的四部分差分。基于荧光图像加卷积神经网络的四部分白细胞分类的分类准确度被量化为 95.4%，基于阻抗变化加循环神经网络的分类准确度为 90.3%，基于荧光图像、阻抗变化、和深度神经网络。基于单细胞荧光成像和阻抗变化与深度神经网络相结合，可以实现几乎 100% 准确度的四部分白细胞分类。