Blood Leukemia Detection using Neural Networks and Fuzzy Logic: A Survey and Taxonomy

Fameshwari Deshmukh*, Amar Kumar Dey**
*PG Scholar, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, Chhattisgarh, India.
**Assistant Professor, Department of Electronics and Telecommunication Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh,India.
Periodicity:July - September'2018


Blood cancer or leukemia detection using microscopic images is a challenging task considering the fact that variations in blood cell patterns are miniscule in nature and human detection may be prone to errors due to inherent deficiencies or anomalies in the dataset or due to human errors. Hence using automated classification has been considered using data pre-processing techniques such as Artificial Neural Networks and Fuzzy Logic. Recently, a new domain of research called neuro-fuzzy systems has garnered a lot of attention due to its efficacy. This paper introduces the challenges faced in the detection and classification of blood leukemia. Along with it, the paper focuses on the various significant contributions in the field by different researchers. This may pave the path for further improvement in accuracy of classification of leukemia.


Leukemia, Artificial Intelligence (AI), Artificial Neural Network (ANN), Fuzzy Logic, Neuro-Fuzzy Systems, Accuracy, Sensitivity.

How to Cite this Article?

Fameshwari, and Dey, A.K., (2018). Blood Leukemia Detection Using Neural Networks And Fuzzy Logic: A Survey And Taxonomy. i-manager’s Journal on Image Processing, 5(3), 34-39.


[1]. Adjouadi, M., Ayala, M., Cabrerizo, M., Zong, N., Lizarraga, G., & Rossman, M. (2010). Classification of leukemia blood samples using neural networks. Annals of Biomedical Engineering, 38(4), 1473-1482.
[2]. Agaian, S., Madhukar, M., & Chronopoulos, A. T. (2014). Automated screening system for acute myelogenous leukemia detection in blood microscopic images. IEEE Systems Journal, 8(3), 995-1004.
[3]. Fraison, J. B., Mekinian, A., Grignano, E., Kahn, J. E., Arlet, J. B., Decaux, O., & Aouba, A. (2016). Efficacy of Azacitidine in autoimmune and inflammatory disorders associated with myelodysplastic syndromes and chronic myelomonocytic leukemia. Leukemia Research, 43, 13- 17.
[4]. Kadono, M., Kanai, A., Nagamachi, A., Shinriki, S., Kawata, J., Iwato, K., & Nagase, R. (2016). Biological implications of somatic DDX41 p. R525H mutation in acute myeloid leukemia. Experimental Hematology, 44(8), 745-754.
[5]. Kumar, R., Srivastava, R., & Srivastava, S. (2015). Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. Journal of Medical Engineering, 2015.
[6]. Manojbhai, D. D., & Rajamenakshi, R. (2016). Large scale image feature extraction from medical image analysis. International Journal of Advance Engineering and Research (IJAERS), 3(1), 62-66.
[7]. Patil, P. R., Sable, G. S., & Anandgaonkar, G. (2014). Counting of WBCs and RBCs from blood images using gray thresholding. International Journal of Research in Engineering and Technology, 3(4), 391-395.
[8]. Pradipkumar, K. K., & Rajamenakshi, R. (2016). Segmentation of large scale medical images using HPC: Classification of methods and challenges. International Journal of Advance Engineering and Research (IJAERS), 3(1), 56-61.
[9]. Putzu, L., Caocci, G., & Ruberto, C. D. (2014). Leucocyte classification for leukaemia detection using image processing techniques. Artificial Intelligence in Medicine, 62(3), 179-191.
[10]. Raje, C., & Rangole, J. (2014, April). Detection of Leukemia in microscopic images using image processing. In Communications and Signal Processing (ICCSP), 2014 International Conference on (pp. 255- 259). IEEE.
[11]. Ravikumar, S. (2016). Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine. Artificial Cells, Nanomedicine, and Biotechnology, 44(3), 985-989.
[12]. Rawat, J., Singh, A., Bhadauria, H. S., & Kumar, I. (2014, December). Comparative analysis of segmentation algorithms for leukocyte extraction in the acute Lymphoblastic Leukemia images. In Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference on (pp. 245-250). IEEE.
[13]. Rawat, J., Singh, A., Bhadauria, H. S., & Virmani, J. (2015). Computer aided diagnostic system for detection of leukemia using microscopic images. Procedia Computer Science, 70, 748-756.
[14]. Saritha, M., Prakash, B. B., Sukesh, K., & Shrinivas, B. (2016, March). Detection of blood cancer in microscopic images of human blood samples: A review. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 596-600). IEEE.
[15]. Supardi, N. Z., Mashor, M. Y., Harun, N. H., Bakri, F. A., & Hassan, R. (2012, March). Classification of blasts in acute leukemia blood samples using k-nearest neighbour. In Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on (pp. 461-465). IEEE.
[16]. Wang, S., Du, S., Atangana, A., Liu, A., & Lu, Z. (2018). Application of stationary wavelet entropy in pathological brain detection. Multimedia Tools and Applications, 77(3), 3701-3714.
[17]. Xing, F., & Yang, L. (2016). Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review. IEEE Reviews in Biomedical Engineering, 9, 234-263.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.