Support vector machines applied to white blood cell recognition

A clinical decision support system known as Leuko has been developed for leukemia diagnosis using a naive Bayes classifier. The system is able to recognize six types of white blood cells (WBC), including a malignancy. This paper investigates the use of support vector machines (SVMs) classifiers to recognize WBC for future leukemia diagnosis. Since SVMs are originally designed for the solution of two class problems, several strategies for their extension to this multiclass task are investigated and compared. The experimental results evidence the potential of SVMs to leukemia diagnosis and indicate that a hierarchical tree-based multiclass strategy can be better suited to a future update of the Leuko system.