Deterministic model for Acute Myelogenous Leukemia classification

Abstract

Leukemia is a type of cancer that affects the blood and the bone marrow. Manual data analysis is time consuming and not accurate. Attempts to build partial/full automated systems based on segmentation and classification of cells are present in literature, but they are still in prototype stage. Most of the existing automatic systems extract features of the sub-images instead of the complete blood smear. [29]. The main objective of this paper is to a) demonstrate that the classification of peripheral blood smear images containing multiple nuclei can be fully automated, b) to validate the segmented images using hold-out cross validation method. The method has been evaluated using a set of 50 images (with 25 abnormal samples and 25 normal samples) obtained from American Society of Hematology [22]. The computer simulations show that the proposed system robustly segments and classifies Acute Myelogenous Leukemia based on complete microscopic blood images. 93.5% of the cases were correctly classified by the program, suggesting that the method yields good results in terms of classification of leukemia. The developed system can be used as ancillary/backup service to the physician.