White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

中文翻译：

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通过生成对抗网络和深度卷积神经网络改善白细胞分类。

白细胞（白细胞）是血液中形成免疫系统的重要组成部分，免疫系统负责抵抗外来元素。五种白细胞包括嗜中性粒细胞，嗜酸性粒细胞，淋巴细胞，单核细胞和嗜碱性粒细胞，其中每种类型构成不同的比例并执行特定功能。能够分类并计算出这些不同的成分对于评估患者的健康状况和感染风险至关重要。通常，实验室实验用于确定白细胞的类型。在显微镜下进行染色过程和对获取的图像进行人工评估很繁琐，容易受到人为错误的影响。此外，一个主要的挑战是无法获得涵盖白细胞形态变化的训练数据，因此训练有素的分类器可以很好地推广。因此，本文研究了图像变换操作和生成对抗网络（GAN），以进行数据增强和最新的深度神经网络（例如VGG-16，ResNet，和DenseNet）将白细胞分为五种类型。此外，我们探索随机初始化DNN的权重或使用在CIFAR-100数据集上预先训练的权重来初始化DNN的权重。与其他需要进行高级图像预处理和在分类之前进行人工特征提取的作品相反，我们的方法可直接对获取的图像进行处理。大量实验结果表明，该方法可以成功地对白细胞进行分类。最好的DNN模型DenseNet-169的验证准确性为98.8％。特别是，我们发现所提出的方法优于依赖于复杂图像处理和手动特征工程的其他方法。与其他需要进行高级图像预处理和在分类之前进行人工特征提取的作品相反，我们的方法可直接对获取的图像进行处理。大量实验结果表明，该方法可以成功地对白细胞进行分类。最好的DNN模型DenseNet-169的验证准确性为98.8％。特别是，我们发现所提出的方法优于依赖于复杂图像处理和手动特征工程的其他方法。与其他需要进行高级图像预处理和在分类之前进行人工特征提取的作品相反，我们的方法可直接对获取的图像进行处理。大量实验结果表明，该方法可以成功地对白细胞进行分类。最好的DNN模型DenseNet-169的验证准确性为98.8％。特别是，我们发现所提出的方法优于依赖于复杂图像处理和手动特征工程的其他方法。验证准确性为98.8％。特别是，我们发现所提出的方法优于依赖于复杂图像处理和手动特征工程的其他方法。验证准确性为98.8％。特别是，我们发现所提出的方法优于依赖于复杂图像处理和手动特征工程的其他方法。