Digital Red Blood Cell Morphology Analysis and Multi-Class Classification powered by Hierarchical AI

Blood Cell morphology identification is a very crucial step in tracing and identifying any abnormality related to hematological disorders. While the traditional method of examining blood smears under a microscope has been prevalent over the years, there is a shift towards leveraging modern computer-edge technologies to assist pathologists in interpreting digitized images of blood smears. However, challenges persist in ensuring the robustness and efficiency of Artificial Intelligence technology utilized in this process. In this paper, we have performed the detail feature extraction and hierarchical Artificial Intelligence models to get accurate results in the classification of 18 types of Red Blood Cells.