Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification

White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis check if WBCs are abnormal or normal, one needs examine numbers shape of WBCs. address this problem, computer-aided procedures have been developed because hematologists perform laborious, expensive, time-consuming process manually. Resultantly, a powerful deep learning model was in present study categorize WBCs, including immature from images peripheral smears. A network based on W-Net, CNN-based method for classification, execute segmentation leukocytes. Thereafter, significant feature maps were retrieved using framework built GhostNet. Then, they categorized ResNeXt with Wildebeest Herd Optimization (WHO)-based method. In addition, Deep Convolutional Generative Adversarial Network (DCGAN)-based data augmentation implemented handle imbalanced issue. validate performance, proposed technique compared existing techniques achieved 99.16%, 99.24%, 98.61% accuracy levels Leukocyte Images Segmentation Classification (LISC), Blood Cell Count Detection (BCCD), single-cell morphological dataset, respectively. Thus, we can conclude that approach is valuable adaptable cell microscopic analysis clinical settings.