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Home»Articles»Classification of Leukemia Image Using Genetic Based K-Nearest Neighbor (G-KNN)

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

Classification of Leukemia Image Using Genetic Based K-Nearest Neighbor (G-KNN)

Author : M. Bennet Rajesh and S. Sathiamoorthy
Volume 7 No.2 July-September 2018 pp 113-117

Abstract

In medical diagnostic system, classification of blood cell is more vigorous to identify the disease. The diseases which are connected with blood is alienated after the categorization of blood cell. Leukemia, a blood cancer that begins in bone marrow. Hence, it must be cured at initial stage and leads to death if left untreated. This paper introduces median filter for noise removing and Genetic based kNN for classification of Leukemia image datasets and features are extracted using gray-level co-occurrence matrix. The outcome of proposed genetic algorithm based kNN is compared with multilayer perceptron and support vector machine. The experimental outcomes evident that proposed combination performs better than the existing approach.

Keywords

Leukemia, K-Nearest Neighbor, Genetic Algorithm, Pre-Processing, Noise Removal, Median Filter approach

Full Text:

References

[1] “Understanding Leukemia”, Leukemia and Lymphoma Society Fighting Blood Cancers.
[2] Chatap, Niranjan and Sini Shibu, “Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 16, No. 5, pp. 79-87, 2014.
[3] Arputha Regina, “Detection of Leukemia with Blood Microscopic Images,” IJIRCCE, Vol.3, Special Issue 3, April 2015.
[4] Tejashri G. Patil and V. B. Raskar., “Blood microscopic image segmentation & acute leukemia detection,” IJERMT, Vol. 4, No. 9, Sep, 2015.
[5] C. Vidhya, P. Saravana Kumar, K. Keerthika, C. Nagalakshmi, and B.Medona devi., “Classification of acute lymphoblastic leukemia in blood microscopic images using SVM”, ICETSH-2015.
[6] Trupti, M., A. Kulkarni-Joshi, and P. D. S. Bhosale, “A fast segmentation scheme for acute lymphoblastic leukemia detection”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, No. 2, pp. 7253-7258, 2014.
[7] Himali P. Vaghela, et al., “Leukemia detection using digital image processing techniques”, Leukemia, Vol. 10, No. 1, pp. 43-51, 2015.
[8] Deore, Sonal G., and Neeta Nemade, “Image analysis framework for automatic extraction of the progress of an infection”, International Journal of Advanced Research in Computer Science and Software, 2013.
[9] Mohapatra, Subrajeet, Dipti Patra, and Sanghamitra Satpathy, “Unsupervised blood microscopic image segmentation and leukemia detection using color based clustering”, International Journal of Computer Information Systems and Industrial Management Applications, Vol. 4, pp. 477-485, 2012.
[10] Emad A. Mohammed, et al., “Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding”, Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on. IEEE, 2013.
[11] Deepshikha Goutam and Sarva Sailaja, “Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier”, IEEE International Conference on Engineering and Technology (ICETECH), 2015.
[12] P. Indira, T. R. Ganesh Babu and K. Vidhya, Detection of Leukemia in Blood Microscope Images, IJCTA, Vol. 9, No. 5, 2016, pp. 63-67.
[13] M. A. Alsalem, A. A. Zaidan and S. Alsyisuf, et al., “A review of the automated detection and classification of acute leukemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations”, Computer Methods and Programs in Biomedicine, 2018.
[14] M. A. Alsalem, A. A. Zaidan, K. I. Mohammed, “Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects”, Journal of Medical Systems, 2018
[15] Zeinab Moshavash, Habibollah Danyali and Mohammad Sadegh Helfroush, “An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images”, Journal of Digital Imaging, 2018.

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In medical diagnostic system, classification of blood cell is more vigorous to identify the disease. The diseases which are connected with blood is alienated after the categorization of blood cell. Leukemia, a blood cancer that begins in bone marrow. Hence, it must be cured at initial stage and leads to death if left untreated. This paper introduces median filter for noise removing and Genetic based kNN for classification of Leukemia image datasets and features are extracted using gray-level co-occurrence matrix. The outcome of proposed genetic algorithm based kNN is compared with multilayer perceptron and support vector machine. The experimental outcomes evident that proposed combination performs better than the existing approach.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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