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dc.contributor.authorAimi, Abdul Nasir-
dc.contributor.authorMohd Yusoff Mashor, Prof. Dr.-
dc.contributor.authorRosline, Hassan-
dc.date.accessioned2014-03-10T09:05:54Z-
dc.date.available2014-03-10T09:05:54Z-
dc.date.issued2013-07-
dc.identifier.citationInternational Arab Journal of Information Technology, vol.10 (4), 2013, pages 356-364en_US
dc.identifier.issn1683-3198-
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/32487-
dc.descriptionLink to publisher's homepage at http://iajit.orgen_US
dc.description.abstractLeukaemia is a cancer of blood that causes more death than any other cancers among children and young adults under the age of 20. This disease can be cured if it is detected and treated at the early stage. Based on this argument, the requirement for fast analysis of blood cells for leukaemia is of paramount importance in the healthcare industry. This paper presents the classification of White Blood Cells (WBC) inside the Acute Lymphoblastic Leukaemia (ALL) and Acute Myelogenous Leukaemia blood samples by using the Multilayer Perceptron (MLP) and Simplified Fuzzy ARTMAP (SFAM) neural networks. Here, the WBC will be classified as lymphoblast, myeloblast and normal cell for the purpose of categorization of acute leukaemia types. Two different training algorithms namely Levenberg-Marquardt and Bayesian Regulation algorithms have been employed to train the MLP network. There are a total of 42 input features that consist of the size, shape and colour based features, have been extracted from the segmented WBCs, and used as the neural network inputs for the classification process. The classification results indicating that all networks have produced good classification performance for the overall proposed features. However, the MLP network trained by Bayesian Regulation algorithm has produced the best classification performance with testing accuracy of 95.70% for the overall proposed features. Thus, the results significantly demonstrate the suitability of the proposed features and classification using MLP and SFAM networks for classifying the acute leukaemia cells in blood sample.en_US
dc.language.isoenen_US
dc.publisherZarqa University, Jordanen_US
dc.subjectAcute leukaemia cellsen_US
dc.subjectFeature extractionen_US
dc.subjectClassificationen_US
dc.subjectMultilayer perceptron neural networken_US
dc.subjectSimplified fuzzy ARTMAP neural networken_US
dc.titleClassification of acute leukaemia cells using multilayer perceptron and simplified fuzzy ARTMAP neural networksen_US
dc.typeArticleen_US
dc.identifier.urlhttp://www.ccis2k.org/iajit/index.php?option=com_content&task=blogcategory&id=87&Itemid=355-
Appears in Collections:School of Mechatronic Engineering (Articles)



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