Supervise Method for Acute Lymphoblastic Leukemia Segmentation and Classification

Abstract

Leukemias are classified as either myelogenous (also called myeloid) or lymphocytic depending on which types of white blood cells are affected. Acute leukemias occur when the bone marrow produces immature white cells, and chronic leukemias occur when the marrow produces mature cells. Acute lymphoblastic leukemia (ALL) is a type of cancer in which the bone marrow makes too many immature lymphocytes (a type of white blood cell). Leukemia may affect red blood cells, white blood cells, and platelets. ALL is most common in childhood, with a peak incidence at 2–5 years of age and another peak in old age. Here is an automatic segmentation technique that uses two-color systems and the clustering algorithm K-means. The proposed approach is evaluated on three public image databases with different characteristics and performance measures: accuracy, speci?city, sensitivity and Kappa index. Segmentation and classification of acute lymphoblastic leukemia can be done by using Supervise Learning Approach. In that hybrid model with color and cluster system will be used.