Correlation based Feature Selection for Diagnosis of Acute Lymphoblastic Leukemia

Acute Lymphoblastic Leukemia (ALL) is a type of cancer characterized by increase in abnormal white blood cells in the blood or bone marrow. This paper presents a methodology to detect ALL automatically using shape features of the lymphocyte cell extracted from its image. We apply Correlation based Feature Selection technique to find a prominent set of features which can be used to predict a lymphocyte cell as normal or blast. The experiments are performed on 260 blood microscopic images of lymphocyte and an accuracy of 92.30% is obtained with a set of sixteen features.