Intelligent Computing Approach for Leukocyte Identification

An automated approach for identifying and classifying leukocytes in microscopic blood smear images have been analyzed in this chapter. The approach involves identifying leukocytes using color-based clustering and then extracting a large set of features from the detected cells. Weighted aggregation-based transposition PSO (WATPSO) is introduced that effectively selects an optimized subset of features that are necessary for correctly classifying healthy cells and blast cells. An acute lymphoblastic leukemia (ALL) benchmark data set is utilized to test the suggested methodology. Remarkably, the result showcases a test accuracy of 97.30% with only 6 optimal features chosen by the WATPSO method. The experimental results highlight the effectiveness of this methodology, highlighting the use of few informative features and excellent test accuracy.