Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier

Blood cancer is a form of cancer which attacks the blood, bone marrow, or lymphatic system. It is diagnosed with a blood test in which specific types of blood cells are counted by hematologist. We considered only acute myelogenous leukemia, which is one of the blood cancer type which categories under acute leukemia and it mostly comes among adults. Need for automatic diagnosis of leukemia arises when doctors recognize cancers under a microscope which has complete manual work and it's not good for the patient. Automatic diagnosis system which helps hematologists for easier identification and early detection of leukemia from blood microscopic images which will improve the chances of survival for the patient. In this proposed system, which mainly composed of four main stages are preprocessed stage, segmentation stage, feature extraction stage and classification stage respectively. This system framework consists simple and known technique such as K-mean clustering, Local Directional path (LDP), and support vector machine (SVM) respectively. The condition of a patient is shown as normal or abnormal status with the help of classifier. The overall system performance is evaluated using the defined parameters such as sensitivity, specificity, f-measure, and precision which used for calculating the accuracy. Ninety microscopic blood images were tested, and the proposed framework managed to obtain 98% accuracy. Finally, we compare the results of some existing systems with our proposed system to show our achievement on accuracy.