Classification of white blood cells based on modified U‐Net and SVM

Abstract

SummaryManual investigation of blood cell count is sometimes erroneous due to interoperability error, fatigue error, requiring expert skill and time consuming too. In particular, investigation of white blood cell (WBC) gains importance in identifying diseases like leukemia, leukopenia, etc. WBC does not possess regular structure because they move throughout the blood stream and hence analyzing WBC and its types for structure and shape is quite challenging. To aid in hematology, this work provides classification of WBC classification based on modified U‐Net and support vector machines (SVM). A modified U‐Net architecture is developed to segment WBC followed by feature extraction and classification by radial basis function‐support vector machine (RBF‐SVM). Experiments indicated that the modified U Net segmentation can detect the WBC nucleus with a dice similarity coefficient of 0.972. The proposed U‐Net‐SVM can recognize WBCs in Raabin‐WBC, LISC, and BCCD datasets with an accuracy of 99.45%, 98.62%, and 98.81%, respectively. Further investigation on leukemia dataset, ALL‐IDB2, revealed an accuracy of 99.42% with 100% sensitivity and specificity. The proposed model can be used to investigate WBCs and hence provide a great support to the hematologists in analyzing the blood smear for various disease identifications.