Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train‐independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train‐dependent dataset and 92.82% for train‐independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train‐independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.

中文翻译：

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使用深度学习方法对白细胞进行分类和分割的综合数据分析

深度学习方法经常用于人类外周血细胞的分类和分割。以前的研究的共同特点是他们使用了多个数据集，但是单独使用它们。尚未发现任何研究可以将两个以上的数据集组合在一起使用。在分类中，通过使用四种不同数据集的混合物来识别五种类型的白细胞。在分割中，确定了四种类型的白细胞，并应用了三种不同的神经网络，包括CNN（卷积神经网络）、UNet和SegNet。本研究的分类结果与相关研究的分类结果进行了比较。平衡精度为98.03%，与训练无关的数据集的测试精度确定为97.27%。对于分割，在细胞核和细胞质检测中，所提出的 CNN 的训练相关数据集的准确率为 98.9%，训练无关的数据集的准确率为 92.82%。在本研究中，所提出的方法表明它可以从与训练无关的数据集中高精度地检测白细胞。此外，它作为一种可用于临床领域的诊断工具很有前景，在分类和分割方面取得了成功的结果。