Classification of Acute Leukemia Based on Multilayer Perceptron

In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samp...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1372 (1), p.12044
Main Authors: Halim, Nurul Hazwani Abd, Mashor, Mohd Yusoff, Hassan, Rosline
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Blood
Classification
Erythrocytes
Feature extraction
Leukemia
Leukocytes
Multilayer perceptrons
Neural networks
Pattern recognition
Physics
Signal processing
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Summary:In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is Levenberg-Marquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4 hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by using LM algorithm is suitable for acute leukemia cells detection in blood sample.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1372/1/012044