White blood cells (Leukocytes) are considered to be an essential part of the human body’s immune system. The count of WBCs is considered to be a parameter for the indication of disease. Over time several methods have been proposed to classify these WBCs into their subtypes namely Neutrophils, Eosinophils, Basophils, Lymphocytes, and Monocytes which helps in the estimation of the body’s WBC count. These methods range from various morphological image processing-based methodologies to advanced deep neural systems. Due to the superior ability of neural systems to achieve the state of the art results more research is been carried out in this field. However, most of the such previously proposed methods have concentrated only in establishing and explaining the overall methodology for achieving high accuracy scores and less emphasis has been made in discussing the impact of modular changes in such methodologies like the impact of various activation functions, optimizers and data pre-processing methods very explicitly for this problem. This has led to a deficiency of work to be carried out with very recently developed activation functions and more essentially optimization algorithms other than backpropagation. It is extremely essential to explore and analyse different modules of the methodology to accelerate future research work further which might possibly help the research community in achieving a much better and efficient solution. This paper compares various architectures and discusses the behaviour and impact of different hyperparameters and proposes a novel methodology by incorporating recently developed swish activation to enhance the results. Unlike previously proposed methods of proposing single better neural network model this paper suggests a good choice of modular changes that could be incorporated in future works to enhance their results.

白细胞（白细胞）被认为是人体免疫系统的重要组成部分。白细胞计数被认为是指示疾病的参数。随着时间的流逝，已经提出了几种方法来将这些白细胞分类为中性粒细胞，嗜酸性粒细胞，嗜碱性粒细胞，淋巴细胞和单核细胞亚型，这有助于估计人体的白细胞计数。这些方法的范围从基于形态学图像处理的各种方法到高级深度神经系统。由于神经系统具有达到最新技术水平的卓越能力，因此在该领域进行了更多研究。然而，多数此类先前提出的方法仅集中于建立和解释用于获得高精度分数的总体方法，而在讨论此类方法中的模块化更改的影响（如各种激活功能，优化器和数据预编译的影响）时，则很少强调处理方法非常明确地针对此问题。这导致了使用最新开发的激活功能以及更重要的是除了反向传播以外的最优化算法无法执行的工作。探索和分析方法论的不同模块以进一步加速未来的研究工作是极其重要的，这可能有助于研究团体获得更好，更有效的解决方案。本文比较了各种体系结构，并讨论了不同超参数的行为和影响，并通过结合最近开发的swish激活来增强结果，从而提出了一种新颖的方法。与先前提出的提出单个更好的神经网络模型的方法不同，本文提出了一种模块化更改的不错选择，可以将其合并到将来的工作中以增强其结果。