Hierarchical AI-Powered Digital White Blood Cell and Platelet Morphology Analysis using Multi-Class Classification

Identifying white blood cell morphology plays a pivotal role in detecting and diagnosing abnormalities associated with hematological disorders especially in cases such as leukemia and other allergies. Although examining blood smears under a microscope has been the conventional approach for years, there's a growing trend towards harnessing cutting-edge computer technologies to aid pathologists in analyzing digitized blood smear images. Yet, ensuring the reliability and effectiveness of Artificial Intelligence technology used in this context remains a challenge. In this study, we conducted comprehensive feature extraction and implemented hierarchical Artificial Intelligence models to achieve precise classification of five types of white blood cells and three types of platelets.