White Blood Cells (WBCs) serve as one of the primary defense mechanisms against various diseases. Therefore, in order to detect blood cancer as well as many other disorders, routine WBC monitoring may be necessary. Numerous studies have proposed automated 4 types of WBC detection through Machine Learning and Deep Learning based solutions. However, transformers based applications, which primarily originated from the field of Natural Language Processing, are very scarce. Our proposed study showcases the applications of Vision Transformers (VTs) for WBC type identification. Firstly, a pre-augmented dataset of nearly 12,500 images was taken. Afterward, two variants of VTs were trained and evaluated on the dataset. Our analysis revealed that the accuracy for all the models ranged from 83% to 85%, making the performance of the VTs equivalent to that of the standard Deep Learning models. Meanwhile, VTs have demonstrated significantly faster learning symptoms during the training phase, which can be useful when one wants to maximize learning through fewer epochs, for example, in a federated learning environment. Finally, the application of Explainable AI (XAI) was visualized on the VTs using Gradient-weighted Class Activation Mapping (GradCam).