Leukaemia screening based on fuzzy ARTMAP and simplified fuzzy ARTMAP neural networks

Abstract

Leukaemia is a life threatening disease that has caused many deaths for the patients below 20 years of age compared to the other types of cancer. The accurate and early detection of leukaemia are the main keys to cure this disease effectively. Therefore, the need for analyzing the blood cells promptly is essential for leukaemia screening. Currently, the procedure for leukaemia screening is performed by haematologists by analyzing the blood cells under the microscope. Since the recognition of the blood cells has been performed manually, it is a time consuming and effortful procedure. As a step to provide the solution to this problem, this paper presents the classification of three different types of white blood cells (WBCs) which are lymphoblast, myeloblast and normal cell inside the Acute Lymphoblastic Leukaemia (ALL), Acute Myelogenous Leukaemia (AML) and normal blood samples by using the Fuzzy ARTMAP (FAM) and Simplified Fuzzy ARTMAP (SFAM) neural networks. Here, an overall of 24 extracted input features that cover the size, shape and colour features have been obtained from each WBC nucleus and fed to both FAM and SFAM networks. Comparison of performance has been made for finding the best classifier that is capable of classifying the WBCs with an optimum result. Overall, the results indicate that SFAM network has produced the highest testing accuracy with classification result of 92.00% by using the overall extracted features compared to the FAM network with classification result of 90.63%.