Leukemia is a type of cancer that can lead to death. The main problem with the leukemia diagnosis lies in the human error in reading the data analysis and the shortage of hematologists available, to perform the data analysis. Creating a computer-aided diagnosis system is one of the
solutions where it can automatically interpret the data so that fast and accurate diagnosis of the disease can be conducted. Recently, most of the researches in the leukemia applied artificial neural network (ANN) for their system seek to classify the blood cells data into normal and abnormal.
This study focuses on the automated detection of acute leukemia and it also attempts to classify the disease into Acute Myelogenous Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). This research utilises a type of neural network (NN) namely Hybrid Multi-layered Perceptron (HMLP) and
trained by Genetic Algorithm (GA) to diagnose the data. The data are obtained from blood cells and 31 features are extracted such as size, radius, perimeter, standard deviation (red, green and blue), the mean of the pixels of colour (red, blue, green), second order moments, third order moments
and affine moments. These features are inserted into the algorithm to calculate and determine the neural network performance. For the analysis, the data is evaluated using the 5-fold cross validation technique. This research shows that HMLP trained with GA provides a better classification
performance with 92.67%, 88.67% and 96.67% of the overall, AML and ALL accuracy respectively.
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Document Type: Research Article
School of Computing, College of Art and Scinces, Universiti Utara Malaysia (UUM), Sintok, Malaysia
Faculty of Electrical Engineering, Universiti Teknologi MARA Pulau Pinang, 13500 Permatang Pauh, P. Pinang, Malaysia
School of Mechatronics Engineering, Universiti Malaysia Perlis, 02600 Pauh, Perlis, Malaysia
Department of Hematology, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
Publication date: April 1, 2017
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ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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