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Detection of acute leukaemia cells using variety of features and neural networks

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Main Authors: A. S., Abdul Nasir, Mohd Yusoff, Mashor, Prof. Dr., H., Rosline
Format: Working Paper
Language:English
Published: Springer-Verlag. 2011
Subjects:
Acute leukemia
Classification
Feature extraction
Multilayer perceptron Neural Network
White blood cells
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/14051
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spelling my.unimap-140512014-06-27T09:47:44Z Detection of acute leukaemia cells using variety of features and neural networks A. S., Abdul Nasir Mohd Yusoff, Mashor, Prof. Dr. H., Rosline Acute leukemia Classification Feature extraction Multilayer perceptron Neural Network White blood cells Link to publisher's homepage at http://springerlink.com/ This paper presents the application of features combination and Multilayer Perceptron (MLP) neural network for classification of individual white blood cells (WBC) inside the normal and acute leukaemia blood samples. The WBC will be classified as either normal or abnormal for the purpose of screening process. There are total 17 main features that consist of size, shape and colour based features had been extracted from segmented nucleus of both types of blood samples and used as the neural network inputs for the classification process. In order to determine the applicability of the MLP network, two different training algorithms namely Levenberg- Marquardt and Bayesian Regulation algorithms were employed to train the MLP network. Overall, the results represent good classification performance by employing the size, shape and colour based features on both training algorithms. However, the MLP network trained using Bayesian Regulation algorithm has proved to be slightly better with classification performance of 94.51% for overall proposed features. Thus, the result significantly demonstrates the suitability of the proposed features and classification using MLP network for acute leukaemia cells detection in blood sample. 2011-10-07T07:43:08Z 2011-10-07T07:43:08Z 2011 Working Paper IFMBE Proceedings, vol. 35, 2011, pages 40-46 978-364221728-9 1680-0737 http://www.springerlink.com/content/jm635t0155362q6g/fulltext.pdf http://hdl.handle.net/123456789/14051 en Proceedings of the 5th Kuala Lumpur International Conference on Biomedical Engineering (BIOMED 2011) Springer-Verlag.
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Acute leukemia
Classification
Feature extraction
Multilayer perceptron Neural Network
White blood cells
spellingShingle Acute leukemia
Classification
Feature extraction
Multilayer perceptron Neural Network
White blood cells
A. S., Abdul Nasir
Mohd Yusoff, Mashor, Prof. Dr.
H., Rosline
Detection of acute leukaemia cells using variety of features and neural networks
description Link to publisher's homepage at http://springerlink.com/
format Working Paper
author A. S., Abdul Nasir
Mohd Yusoff, Mashor, Prof. Dr.
H., Rosline
author_facet A. S., Abdul Nasir
Mohd Yusoff, Mashor, Prof. Dr.
H., Rosline
author_sort A. S., Abdul Nasir
title Detection of acute leukaemia cells using variety of features and neural networks
title_short Detection of acute leukaemia cells using variety of features and neural networks
title_full Detection of acute leukaemia cells using variety of features and neural networks
title_fullStr Detection of acute leukaemia cells using variety of features and neural networks
title_full_unstemmed Detection of acute leukaemia cells using variety of features and neural networks
title_sort detection of acute leukaemia cells using variety of features and neural networks
publisher Springer-Verlag.
publishDate 2011
url http://dspace.unimap.edu.my/xmlui/handle/123456789/14051
_version_ 1643791130899251200
score 13.222552

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