Interpolative Leishman-Stained transformation invariant deep pattern classification for white blood cells

Abstract

Blood analysis is regarded as the one of the most predominant examinations in medicine field to obtain patient physiological state. A significant process in the classification of white blood cells (WBC) is blood sample analysis. An automatic system that is potential of identifying WBC aids the physicians in early disease diagnosis. In contrast to previous methods, thus resulting in trade-off among computational time (CT) and performance efficiency, an interpolative Leishman-stained multi-directional transformation invariant deep classification (LSM-TIDC) for WBC is presented. LSM-TIDC method discovers possibilities of interpolation and Leishman-stained function, because they require no explicit segmentation, and yet they eliminated false regions for several input images. Next, with the preprocessed images, optimal and relevant features are extracted by applying multi-directional feature extraction. To identify and classify blood cells, a system is developed via the implementation of transformation invariant model for extraction of nucleus and subsequently performs classification through convolutional and pooling characteristics. The proposed method is evaluated by extensive experiments on benchmark database like blood cell images from Kaggle. Experimental results confirm that LSM-TIDC method significantly captures optimal and relevant features and improves the classification accuracy without compromising CT and computational overhead.