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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2017 Vol.7(6): 218-222 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.6.650

A Hybrid Classification Scheme Using 2D-SWT and SVM for the Detection of Acute Lymphoblastic Leukemia

Sonali Mishra, Banshidhar Majhi, and Pankaj Kumar Sa
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.

Index Terms—Leukemia, CAD system, 2D-SWT, Support vector machine.

The authors are with the National Institute of Technology, Rourkela, India, 769008 (e-mail: smishra.nitrkl@gmail.com, bmajhi@nitrkl.ac.in, pankajksa@nitrkl.ac.in).

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Cite: Sonali Mishra, Banshidhar Majhi, and Pankaj Kumar Sa, "A Hybrid Classification Scheme Using 2D-SWT and SVM for the Detection of Acute Lymphoblastic Leukemia," International Journal of Machine Learning and Computing vol. 7, no. 6, pp. 218-222, 2017.

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