TY - JOUR
AU - 
AB - Paper—Detection and Classification of White Blood Cells Through Deep Learning Techniques Detection and Classification of White Blood Cells Through Deep Learning Techniques https://doi.org/10.3991/ijoe.v16i15.15481 ( ) M. Samir Abou El-Seoud , Muaad Hammuda Siala, Gerard McKee The British University, Cairo, Egypt Samir.elseoud@bue.edu.eg Abstract—Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consum- ing and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this pa- per, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the 
TI - Detection and Classification of White Blood Cells through Deep Learning Techniques
JF - International Journal of Online and Biomedical Engineering (iJOE)
DO - 10.3991/ijoe.v16i15.15481
DA - 2020-12-15
UR - https://www.deepdyve.com/lp/unpaywall/detection-and-classification-of-white-blood-cells-through-deep-BnyCXexJ0v
DP - DeepDyve
ER -