Deep learning shown its potential in a variety of medical applications and proved as a count on by people as a step ahead approach compared to traditional machine learning models. Moreover, the other implementations of these models such as the convolutional neural networks (CNNs) provide extensive applications in the field of medicine, which usually involves processing and analysis of a large dataset. This paper aims to create a CNN model which can solve the problem of white blood cell subtyping which is a daunting one in clinical processing of blood. The manual classification of white blood cells in laboratory is a time-consuming process which gives rise to the need for an automated process to perform the task. A CNN-based machine learning model is developed to classify the leukocytes into their proper subtypes by performing tests on a dataset of around twelve thousand images of leukocytes and their types, and a wide range of parameters is evaluated. This model can automatically classify the white blood cells to save manual labor, time and improve efficiency. Further, pretrained models like Inception-v3, VGGNet and AlexNet are used for the classification, and their performance is compared and analyzed.