Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis

Abstract

This paper presents a simple and efficient computer-aided diagnosis method to classify Chronic Myeloid Leukemia (CML) cells based on microscopic image processing. In the proposed method, a novel combination of both typical and new features is introduced for cl assification of CML cells. Next, an eff ective decision tree classifier is pro- posed to classify CML cells into eight groups. The proposed method was evaluated on 1730 CML cell images containing 714 cells of non-cancerous bone marrow aspiration and 1016 cells of cancerous peripheral blood smears. The performance of the proposed classification method was compared to manual labels made by two experts. The average values of accuracy , specificity and sensitivity were 99.0 %, 99.4 % and 98.3 %, respectively for all groups of CML. In addition, Cohen's kappa coefficient demonstrated high conformity, 0.99, between joint diagnostic results of two experts and the obtained results of the proposed approach. According to the obtained results, the suggested method has a high capability to cla ssify effective cells of CM L and can be applied as a simple, affordable and reliable computer-aided diagnosis tool to help pathologists to diagnose CML.