Automated Detection of Acute Leukemia Using K-Means Clustering Algorithm

Abstract

Detection of ALL can be done through the analysis of white blood cells (WBCs) called leukocytes. Usually, the analysis of blood cells is performed manually by skilled operators, have numerous drawbacks, such as slow analysis, a non-standard accuracy and skill of the operator. Hence many automated systems are using in order to analyze and classify the blood cells. This paper focuses on an automatic system based on image processing algorithms for the classification of blood cells for detection of Acute Lymphocytic Leukemia (ALL). Experiments were ran using 20 models with PCA and seven models namely Medium KNN, Coarse KNN, Cosine KNN, Cubic KNN, Weighted KNN, Ensemble Boosted trees and Ensemble Bagged trees had 99.9% accuracy. These models are evaluated based on the prediction speed, training time, confusion matrix and ROC. Of all models, the weighted KNN classifier is best when using PCA.