Abstract
With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%.
Highlights
In the fields of haematology and infectious diseases, classifying different kinds of blood cells can be used as a tool in diagnosis
Abdul Nasir et al [10], proposed application of MLP and simplified fuzzy ARTMAP (SFAM) neural networks for classifying the individual white blood cells (WBC) as lymphoblast, myeloblast and normal cell based on the extracted features from both acute lymphoblastic leukaemia (ALL) and acute myelogenous leukaemia (AML) blood samples
The MLP trained by Back Propagation (BP) algorithm have been used to classify five types of WBC, namely, (1) neutrophils, (2) basophils, (3) eosinophils, (4) lymphocytes, and (5) monocytes
Summary
In the fields of haematology and infectious diseases, classifying different kinds of blood cells can be used as a tool in diagnosis. Due to the different morphological features of the white blood cells, manual classification of such cells is a cumbersome process, which is time-consuming and susceptible to human error as it is mostly related to the haematologists’ experience. Abdul Nasir et al [10], proposed application of MLP and simplified fuzzy ARTMAP (SFAM) neural networks for classifying the individual WBC as lymphoblast, myeloblast and normal cell based on the extracted features from both acute lymphoblastic leukaemia (ALL) and acute myelogenous leukaemia (AML) blood samples. The multilayer perceptron back-propagation MLP-BP neural network is used to classify the most known five types of WBC that have been segmented from blood smear microscopic images using the most distinguishing features. The first stage is image segmentation, the second stage is labelling that returns the number and location of each WBC, and the third stage is extracting descriptive features measured from the segmented cells
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