Detection of white blood cells using optimized qGWO

This paper presents an optimized quantum Grey Wolf Optimization algorithm (qGWO), which is an enhanced version of the Grey Wolf optimization algorithm for feature selection of blood cells, which can further used for the detection of WBCs. White blood cells count in the human body determines the immune system of the human body. A deviation in the count of WBCs from the normal cell count in the human body may indicate an abnormal condition. The proposed model uses a quantum grey wolf optimization algorithm for the detection of White Blood cells among the dataset of various types of blood cells. Quantum Grey Wolf algorithm is used to find the minimal set of optimal features from the set of available features to detect the White Blood Cells optimally. As the ordinary Grey Wolf Optimization algorithm also used to find the minimal set of optimal features, but the features selected by qGWO are better in terms of computational time. Further, several classification algorithms such as Support Vector Machine (SVM), Random Forest algorithm, K Nearest Neighbor(KNN) algorithm are applied to the model to predict its accuracy for the selected subset of features after feature selection. The performance of several classifiers is compared, and the model attained the maximum accuracy of 97.8% using KNN with minimum computational time. The result obtained shows that the algorithm proposed is capable of finding an optimal subset of features and maximizing the accuracy.

Automated summary

An optimized quantum Grey Wolf Optimization algorithm is proposed for feature selection of blood cells, especially for white blood cells detection. The algorithm outperforms the traditional Grey Wolf Optimization algorithm in both accuracy and computational time.