An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework

White Blood Cells are essential in keeping track of a person’s health. However, the pathologist’s experience will determine how blood smear is evaluated. Furthermore, it still challenging to classify WBCs accurately because they have various forms, sizes, and colors due distinct cell subtypes labeling methods. As result, powerful deep learning system for WBC categorization based on MobilenetV3-ShufflenetV2 described this research. Initially, images segmented using an efficient Pyramid Scene Parsing Network (PSPNet). Following that, MobilenetV3 Artificial Gravitational Cuckoo Search (AGCS)-based technique used extract select global local features from images. Finally, divided into five classes ShufflenetV2 model. The proposed approach evaluated count detection (BCCD) Raabin-Wbc datasets achieves 99.19% 99% accuracy, respectively. Moreover, results satisfactory when compared existing algorithms.