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

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