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Classification of immature white blood cells in acute lymphoblastic leukemia L1 using neural networks particle swarm optimization by Rosi Indah Agustin; Agus Arif; Usi Sukorini is a Computer Science article available to read on EtoBox.

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Acute Lymphoblastic Leukemia (ALL) is a type of leukemia that is related to a large number of lymphoblast cells in the peripheral blood and bone marrow. The initial step in diagnosing the disease is an individual immature White Blood Cells (WBC) assessment by the hematologists. Visual interpretation and detection of immature WBC is a time-consuming and burdensome task for hematologists. The reliable and confident examination of the ALL blood specimen relies on a valid classification of lymphoblast cells. In this paper, we proposed two-stages Artificial Neural Networks integrated with the Particle Swarm Optimization method to classify the immature WBC in ALL patients. The proposed method includes binary classification of lymphoid cells in the first stages and binary classification of lymphoblast cells in the second stages. In this study, we have used five peripheral blood specimen samples obtained from Sardjito Hospital ALL dataset to develop the proposed model. The proposed model consists of data preprocessing, features selection, features extraction, and two-stage classification. The performance of our approach is compared with the common backpropagation neural networks classifica

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Author

Rosi Indah Agustin; Agus Arif; Usi Sukorini

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)