Categorizing white blood cells (WBCs) in blood specimens is crucial for early disease detection in hematology and pathology. Numerous research endeavors have been undertaken to investigate the precise classification of WBCs in microscopic blood smear images, utilizing machine learning and deep learning methodologies. However, the notable accuracy achieved by most of these techniques relies on the assumption that the conditions under which the test microscope images are acquired closely resemble those of the training images, including the use of identical microscopes and consistent lighting conditions. The primary aim of this research is to design a novel network layer that can be used as a component in combination with other backone network, and improve the generalization ability of this backbone for classification tasks on WBC data. In this investigation, we predominantly employ the color invariance technique to fashion a trainable convolutional layer, which serves as the tool for extracting edge features from the original image. This layer is amenable to integration with various backbone architectures. Ultimately, the model was trained utilizing the Raabin-WBC dataset, and its efficacy was evaluated by appraising its performance on WBC images acquired through a variety of microscope instruments.