Automated Classification of Blasts in Acute Leukemia Blood Samples Using Hmlp Network
نویسندگان
چکیده
This paper presents a study on classification of blasts in acute leukemia blood samples using artificial neural network. In acute leukemia there are two major forms that are acute myelogenous leukemia (AML) and acute lymphocytic leukemia (ALL). Six morphological features have been extracted from acute leukemia blood images and used as neural network inputs for the classification. Hybrid Multilayer Perceptron (HMLP) neural network was used to perform the classification task. The Hybrid Multilayer Perceptron(HMLP) neural network is trained using modified RPE(MRPE) training algorithm for 1474 data samples. The Hybrid Multilayer Perceptron (HMLP) neural network produces 97.04% performance accuracy. The result indicates the promising capabilities and abilities of the Hybrid Multilayer Perceptron (HMLP) neural network using modified RPE (MRPE) training algorithm for classifying and distinguishing the blasts from acute leukemia blood samples.
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