Recognition of Blood Cancer Using Different Classification Techniques
Keywords:Blood cancer detection, C-Means clustering, K-Means clustering, Genetic algorithm
Several image processing methods or applications have been established to get the requisite details from microscopic images for the fast and cost effective development of patient diagnosis. A type of leukemia that is more frequent in children is Acute Lymphoblastic Leukemia (ALL). The word 'Acute' suggests that leukemia can develop rapidly and can lead to fatal death within a few months if not managed. Because of its nonspecific existence, erroneous diagnosis refers to the effects and manifestations of everything. And though it is challenging for hematologists to identify leukemia cells, manual sorting of blood cells is not only time-consuming but also unreliable. Consequently, early diagnosis of leukemia helps in presenting the patient with the necessary care. The mechanism recommends individuals in the blood picture the leucocytes from the blood cells as a response to this issue, and then select the lymphocyte cells. The morphological index of certain cells is measured and the involvement of leukemia is eventually classified. A literature review on several methods used to identify cancer cells has been carried out in this article.
C. Raje, J. Rangole, "Detection of Leukemia in microscopic images using image processing," International Conference on Communications and Signal Processing (ICCSP), pp. 255-259, April 2014.
K. Deb, A. R. Reddy, “Reliable classification of two class cancer data using evolutionary algorithms”, Bio Systems, Vol. 72, pp.111–129, 2003.
S. Mohapatra, S. S. Samanta, D. Patra and S. Satpathi, “Fuzzy based Blood Image Segmentation for Automated Leukemia Detection”, International conference on devices and communications, Mesra, pp. 1-5, 2011.
N. Patel, A. Mehra, “Automated Detection of Leukimia using microscopic images”, Procedia computer science, Vol. 58, pp. 635-642, 2015.
I. Jafar, H. Ying, A. F. Shields, O. Muzik, “Computerized Detection of Lung Tumors in PET/CT Images”, International Conference of the IEEE Engineering in Medicine and Biology Society, New York, pp. 2320-2323, 2006.
M. Nakao, A. Kawashima, M. Kokubo, K. Minato “Simulating Lung Tumor Motion for Dynamic Tumor Tracking Irradiation”, Nuclear Science Symposium Conference Record, Honolulu, pp. 4549-4551, 2007.
E. Donald “Introduction to Data Mining for Medical Informatics”, Clinics in Laboratory Medicine, Vol. 28, No. 1, pp. 9-35, 2008.
R. Zhang, Y, Katta, “Medical Data Mining,” Data Mining and Knowledge Discovery, pp. 305-308, 2002.
I. M. Mullins “Data mining and clinical data repositories: Insights from patient data set,” Computers in Biology and Medicine, vol. 36, pp. 1351- 1377, 2006.
Zadeh, H. Ghayoumi, S. Janianpour, and J. Haddadnia, "Recognition and Classification of the Cancer Cells by Using Image Processing and Lab VIEW”, International Journal of Computer Theory and Engineering, Vol. 5, No. 1, pp. 104-107, 2013.
Z. Diana, Y. Pigovsky, and P. Bykovyy. "Canny-based method of image contour segmentation”, International Journal of Computing, Vol. 15, No. 3 pp: 200-205, 2016.
Biswas, Ranita, and Jaya Sil. "An improved canny edge detection algorithm based on type-2 fuzzy sets”, Procedia Technology, Vol. 4, pp: 820-824, 2012.
Z. Wang, D. Ziou, C. Armenakis, D. Li, & Q. Li “A comparative analysis of image fusion methods”, IEEE transactions on geosciences and remote sensing, Vol. 43, No. 6, pp. 1391-1402, 2005.
H. Ghassemian “A review of remote sensing image fusion methods”, Information Fusion, Vol. 32, pp. 75-89, 2016.
F. Vollnhals, J. N. Audinot, T. Wirtz, M. M. Bonin, I. Fourquaux, B. Schroeppel & S. Eswara “Correlative microscopy combining secondary ion mass spectrometry and electron microscopy: comparison of intensity–hue–saturation and Laplacian pyramid methods for image fusion”, Analytical chemistry, Vol. 89, No. 20, pp. 10702-10710, 2017.
K. C. Bhataria and B. K. Shah, "A Review of Image Fusion Techniques," International Conference on Computing Methodologies and Communication, Erode, pp. 114-123, 2018.
L. B. Kone, N. M. Kunda, T. B. Tran, & A. V. Maker “Surgeon-placed continuous wound infusion pain catheters markedly decrease narcotic use and improve outcomes after pancreatic tumor resection”, Annals of surgical oncology, pp. 1-9, 2020.
S. Belciug, & F. Gorunesc, “A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence”, Expert Systems, Vol. 30, No. 3, pp. 243-254, 2013.
P. D. Cesar, R. P. Ramos, and M. Z. D. Nascimento “Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm”, Computer methods and programs in biomedicine, Vol. 114, No. 1, pp. 88-101, 2014.
P. Bhuvaneswari & A. B. Therese “Detection of cancer in lung with K-NN classification using genetic algorithm”, Procedia Materials Science, Vol. 10, pp. 433-440, 2014.
B. Zheng, S. W. Yoon, & S. S. Lam “Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms”, Expert Systems with Applications, Vol. 41, No. 4, pp. 1476-1482, 2014.
B. C. Patel, & G. R. Sinha “An adaptive K-means clustering algorithm for breast image segmentation”, International Journal of Computer Applications, Vol. 10, No. 4, pp. 35-38, 2010.
A. Sathish, and J. M. Sundaram. "A comparative study on K-means and fuzzy C-means algorithm for breast cancer analysis." International Journal of Computational Intelligence and Informatics, Vol. 4, No. 1, pp: 54-58, 2014.
R. Preetha, & G. R. Suresh “Performance analysis of fuzzy c means algorithm in automated detection of brain tumor”, World Congress on Computing and Communication Technologies, pp. 30-33, 2014.
How to Cite
Copyright (c) 2020 International Transactions on Electrical Engineering and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.