Classification of Acute Leukaemia Cells using Multilayer Perceptron and Simplified Fuzzy ARTMAP Neural Networks
Aimi Abdul Nasir1, Mohd Yusoff Mashor1, and Rosline Hassan2
1Electronic and Biomedical Intelligent Systems Research Group, Universiti Malaysia Perlis, Malaysia
2Department of Haematology, Universiti Sains Malaysia, Malaysia
1Electronic and Biomedical Intelligent Systems Research Group, Universiti Malaysia Perlis, Malaysia
2Department of Haematology, Universiti Sains Malaysia, Malaysia
Abstract: Leukaemia 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.
Keywords: Acute leukaemia cells, feature extraction, classification, multilayer perceptron neural network, simplified fuzzy ARTMAP neural network.
Received September 19, 2011; accepted December 30, 2011; published online August 5, 2012