White Blood Cell Classification based on Gray Level Co-occurrence Matrix with Zero Phase Component Analysis Approach

Abstract

This paper presents an approach to WBC classification by employing Gray Level Co-occurrence Matrix (GLCM) along with the Analysis of Variance (ANOVA) test and Zero Phase Component Analysis (ZCA) whitening. Moreover, the performance is evaluated through the K- Nearest Neighbor (K-NN) classifier. The proposed approach has achieved an accuracy of 94.25% on the Blood Cell Count and Detection (BCCD) dataset for the classification of four categories of WBC namely, lymphocytes, monocytes, neutrophils, and eosinophils. The experimental results reveal an improvement in accuracy (11.05, 8.15, and 14.25%) in comparison to the state-of-the-art approaches i.e., Watershed segmentation, Local Binary Pattern (LBP), and Extreme Learning Machines (ELMs) respectively.