Artificial Intelligence Enabled Classification of Microscopic Blood Samples for Leukemia Diagnosis

White blood cell (WBC) cancer, or leukemia, harms the body's bone marrow and blood. If not detected at an early stage, the illness may be fatal. When clinically significant features and biologically explicable methods are employed to determine the severity of the disease and identify malignancy, machine-controlled identification of cancerous cells from microscopic diagnostic assay images of blood samples helps alleviate leukemia's diagnostic issues and yields better results. This proposed method used deep learning CNN model YOLOv5s which is fast and lightweight. This paper achieved a Metric precision of 94% and a mean average precision (mAP@0.5) of 98%.