ADVANCED BLOOD CELL CLASSIFICATION - A CNNBASED ANALYSIS FOR PRECISE IDENTIFICATION

Abstract

White blood cells, also known as leukocytes, perform a crucial role in the human body by
boosting immunity and combating infectious infections. The classification of white blood cells is critical in the
detection of disease in an individual. The classification can also help identify disorders caused by immune
system abnormalities, such as infections, allergies, anemia, leukemia, cancer, Acquired Immune Deficiency
Syndrome (AIDS), and so on.
This classification will help hematologists distinguish the types of white blood cells seen in the
human body and identify the underlying cause of disorders. There is now a significant amount of research being
conducted in this topic. Given the importance of WBC classification, we will use a deep learning technique
known as Convolution Neural Networks (CNN) to categorize WBC images into subtypes such as neutrophil,
eosinophil, lymphocyte, and monocyte. In this study, we will present the results of numerous experiments
carried out on the Blood Cell Classification and Detection (BCCD) dataset using CNN..