Abstract：
The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home-developed approaches. In this study, both traditional machine learning of K-means clustering versus deep learning of U-Net, U-Net + ResNet18, and U-Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep-learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell-type classification of ALL-IDB2 and PCB-HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.
摘要：
白细胞分化的金标准是手工检查血涂片,这不仅是时间和劳动力密集型的，而且容易受到人为错误的影响。至于自动分类，仍然没有细胞分割的比较研究，特征提取,和细胞分类，将各种机器和深度学习模型与家庭开发的方法进行比较。在这项研究中,传统的K均值聚类的机器学习与U-Net的深度学习，使用U-Net+ResNet18和U-Net+ResNet34进行细胞分割，CellaVision数据集的分割精度为94.36%与99.17%，BCCD数据集的分割精度为93.20%与98.75%，证实深度学习在白细胞分类方面比传统机器学习产生更高的性能。此外,一系列深度学习方法，包括AlexNet,采用VGG16和ResNet18进行白细胞的特征提取和细胞分类,产生91.31%的分类准确率，97.83%,和100%的CellaVision以及81.18%，BCCD的91.64%和97.82%，证实了神经网络在白细胞分类中增加深度的能力。至于示威，本研究进一步对ALL-IDB2和PCB-HBC数据集进行了细胞类型分类，在所有文献中产生100%和98.49%的高精度，验证本研究中使用的深度学习模型。
