Leukocyte Disorder Detection and White Blood Cell Subtype Classification Using CNN

White blood cell classification, which indicates possible diseases and illnesses, is an important function in clinical diagnosis. Every subtype of white blood cells has its own benefits for our body. Additionally, low levels of these subtypes can lead to deficiencies and diseases. Standard machine learning methodologies show promising results; however, they still fail to achieve accurate detection capabilities. We designed a WBC classification method that uses the Convolutional Neural Networks (CNN) structure as the basis for deep learning architecture. Our method is based on the CNN architecture for the classification of WBC types in this paper. Our methodology analyzes WBC photographs to generate distinct outputs that provide the exact subtype and encompass diagnostic accuracy metrics, along with cell count and disease risk alerts stemming from detected WBC irregularities. We can use the Logitech webcam for blood cell analysis. The proposed model demonstrates significant efficiency in analyzing WBC images, making it a valuable tool for medical diagnostics.