A hybrid classification scheme using 2D-SWT and SVM for the detection of acute lymphoblastic leukemia

Abstract

Acute lymphocytic leukemia (ALL) is a heterogeneous disease that differs considerably in their cellular and molecular characteristics and also affects a larger proportion of world population Advanced and specific techniques are available for classifying leukemia types however they are exceptionally costly and not accessible to many doctor's facilities in developing nations. Image processing can be a way to detect the disease more precisely and conjointly takes a trifle time. This paper presents a hybrid scheme for identification and classification of ALL. The suggested scheme utilizes 2D-SWT to extricate the texture features from the blood smear. Later on, the extracted features are fed to SVM classifier to get the classification results. The experimental results for leukemia classification show that the suggested method outperforms other standard classifiers regarding accuracy. The accuracy is found to be 99.56% with the help of SVM-R classifier.