In this work, image processing and deep learning mechanisms are used to locate and classify the
White Blood Cells based on their categories. The White Blood Cells which are classified are counted and
compared with the standard range of the types available in the human blood sample. By comparing the
availability of White Blood Cells types, the normal and the abnormal blood samples are predicted accordingly.
The dataset of the normal blood sample is obtained from the laboratory in biotechnology department and the
datasets used for training in Convolutional Neural Network are attained from the website Leukocyte Images for
Segmentation and Classification (LISC). This will increase efficiency and reduce the doctor’s burden as
traditional manual counting is dull, tedious, and possibly subjective.
Background: White Blood Cells (WBCs) are also called leukocytes or leucocytes. These are cells of the immune
system that are involved in the body against both infectious disease and foreign materials. These cells help fight
infections by attacking bacteria, viruses, and germs that invade the body. All leukocytes are produced and
derived from a multipotentcell. This is also known as a hematopoietic stem cell. They live for about three to four
days in
the human body. The segmentation has to be done to extract the nucleus of white blood cell image.