Detection and Classification of White Blood Cells through Deep Learning Techniques

Authors

  • Samir Abou El-Seoud The British University in Egypt - BUE
  • Muaad Hammuda Siala Graduate student, BUE
  • Gerard McKee The British University in Egypt - BUE

DOI:

https://doi.org/10.3991/ijoe.v16i15.15481

Keywords:

Image Processing, Convolutional Neural Network, Deep learning, Histology

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 consuming 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 paper, 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 high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

Author Biographies

Samir Abou El-Seoud, The British University in Egypt - BUE

BUE, Informatics and Computer Science. Professor

Muaad Hammuda Siala, Graduate student, BUE

Graduate student, ICS, BUE

Gerard McKee, The British University in Egypt - BUE

BUE, Informatics and Computer Science. Professor

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Published

2020-12-15

How to Cite

Abou El-Seoud, S., Siala, M. H., & McKee, G. (2020). Detection and Classification of White Blood Cells through Deep Learning Techniques. International Journal of Online and Biomedical Engineering (iJOE), 16(15), pp. 94–105. https://doi.org/10.3991/ijoe.v16i15.15481

Issue

Vol. 16 No. 15 (2020)

Section

Papers

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