Application of ensemble artificial neural network for the classification of white blood cells using microscopic blood images

Abstract

In order to overcome the problems of manual diagnosis in recognising the morphology of blood cells, the automated analysis is frequently used by a pathologist. So this work gives a semi-automated technique to identify and classify white blood cell. In this work, a k-means clustering algorithm is used to segment the nucleus by upgrading the district of the white blood cell nucleus and stifling the other components of the blood smear images. From each cell, various shape, chromatic and texture features are extracted. This feature set was used to train the classifier to determine different classes of WBC. Performance of this model indicates that CAC system design based on the ensemble artificial neural network is the most suitable model for the four class white cell classification, with an accuracy of 95%. The proposed method represents a medicinal method to avoid the plentiful drawbacks associated with the labour-intensive examination of WBCs.