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Survey of Leukemia Cancer Cell Detection Using Image Processing

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1567))

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Abstract

Cancer is the development of abnormal cells that divide at an abnormal pace, uncontrollably. Cancerous cells have the ability to destroy other normal tissues and can spread throughout the body. Cancer cells can develop in various parts of the body. The paper focuses on leukemia which is a type of blood cancer. Blood cancer usually start in the bone marrow where the blood is produced in the body. The types of blood cancer are: Leukemia, Non-Hodgkin lymphoma, Hodgkin lymphoma, and Multiple myeloma. Leukemia is a type of blood cancer that originates in the bone marrow. Leukemia is seen when the body produces an abnormal amount of white blood cells that hinder the bone marrow from creating red blood cells and platelets. Several detection methods to identify the cancerous cells have been proposed. Identification of the cancer cells through cell image processing is very complex. The use of computer aided image processing allows the images to be viewed in 2D and 3D making it easier to identify the cancerous cells. The cells have to undergo segmentation and classification in order to identify the cancerous tumours. Several papers propose segmentation methods, classification methods and some propose both. The purpose of this survey is to review various papers that use either conventional methods or machine learning methods to detect the cells as cancerous and non-cancerous.

Supported by National Institute of Technology Karnataka.

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References

  1. ALL-IDB Acute Lymphoblastic Leukemia Image Database for Image Processing (2011). https://homes.di.unimi.it/scotti/all/

  2. Support Vector Machines (SVM)—An Overview—by Rushikesh Pupale—Towards Data Science (2018). https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989

  3. Chapter 1 - Diagnosis and classification of leukaemias—OncologyPRO (2021). https://oncologypro.esmo.org/education-library/essentials-for-clinicians/leukaemia-and-myeloma/chapter-1-diagnosis-and-classification-of-leukaemias

  4. C_NMC_2019 Dataset: ALL Challenge dataset of ISBI 2019 - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki, February 2021. https://wiki.cancerimagingarchive.net/display/Public/C_NMC_2019Dataset%3AALLChallengedatasetofISBI2019

  5. ImageBank—Home—Regular Bank (2021). https://imagebank.hematology.org/

  6. Leukemia Classification—Kaggle (2021). https://www.kaggle.com/andrewmvd/leukemia-classification

  7. Leukemia: Symptoms, Types, Causes & Treatments (2021). https://my.clevelandclinic.org/health/diseases/4365-leukemia

  8. What is Cancer? - National Cancer Institute, May 2021. https://www.cancer.gov/about-cancer/understanding/what-is-cancer

  9. Abas, S.M., Abdulazeez, A.M.: Detection and classification of leukocytes in Leukemia using YOLOv2 with CNN. Asian J. Res. Comput. Sci. 64–75 (2021). https://doi.org/10.9734/ajrcos/2021/v8i330204

  10. Abdullah, N.A.A., Ibrahim, M.A.M., Haider, A.S.M.: Automatic segmentation for Acute Leukemia Cells from Peripheral Blood Smear images. Int. J. Creative Res. Thoughts 9(4), 2248–2264 (2021)

    Google Scholar 

  11. Ali, N.O.: A Comparative study of cancer detection models using deep learning (2020). http://hdl.handle.net/2043/32148

  12. Amin, M.M., Kermani, S., Talebi, A., Oghli, M.G.: Recognition of acute lymphoblastic leukemia cells in microscopic images using K-means clustering and support vector machine classifier. J. Med. Sign. Sens. 5(1), 49 (2015). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4335145/

  13. Asadi, F., Putra, F.M., Sakinatunnisa, M.I., Syafria, F., Okfalisa, Marzuki, I.: Implementation of backpropagation neural network and blood cells imagery extraction for acute leukemia classification. In: 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp. 106–110. IEEE, November 2017. https://doi.org/10.1109/ICICI-BME.2017.8537755

  14. Asanka, D.: No Title. Implement Artificial Neural Networks (ANNs) (2021)

    Google Scholar 

  15. Bengtsson, E., Wählby, C., Lindblad, J.: Robust cell image segmentation methods. Pattern Recogn. Image Anal. 14(2), 157–167 (2004). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464.5405 &rep=rep1 &type=pdf

  16. Bibi, N., Sikandar, M., Ud Din, I., Almogren, A., Ali, S.: IoMT-based automated detection and classification of leukemia using deep learning. J. Healthc. Eng. 2020, 1–12 (2020). https://doi.org/10.1155/2020/6648574

  17. Brazier, Y.: Tumors: benign, premalignant, and malignant, August 2019. https://www.medicalnewstoday.com/articles/249141

  18. Chen, C.L., et al.: Deep learning in label-free cell classification. Sci. Rep. 6(1), 21471 (2016). https://doi.org/10.1038/srep21471

  19. Das, B.K., Dutta, H.S.: GFNB: Gini index-based Fuzzy Naive Bayes and blast cell segmentation for leukemia detection using multi-cell blood smear images. Med. Biol. Eng. Comput. 58(11), 2789–2803 (2020). https://doi.org/10.1007/s11517-020-02249-y

  20. Das, P.K., Meher, S.: An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia. Expert Syst. Appl. 183, 115311 (2021). https://doi.org/10.1016/j.eswa.2021.115311

  21. Dwivedi, A.K.: Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput. Appl. 29(12), 1545–1554 (2018). https://doi.org/10.1007/s00521-016-2701-1

  22. Eldosoky, M.A., Moustafa, H.M.: Experimental detection of the leukemia using UWB. In: Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation, pp. 1–2, July 2012. https://doi.org/10.1109/APS.2012.6349100

  23. Goel, N., Yadav, A., Singh, B.M.: Medical image processing: a review. In: 2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH), pp. 57–62, November 2016. https://doi.org/10.1109/CIPECH.2016.7918737

  24. Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Sci. 286(5439), 531–537 (1999). https://doi.org/10.1126/science.286.5439.531

  25. Gupta, A., Gupta, R.: SN-AM Dataset: white blood cancer dataset of B-ALL and MM for stain normalization (2019). https://doi.org/10.7937/tcia.2019.of2w8lxr

  26. Hamidah, Rustam, Z., Utama, S., Siswantining, T.: Multiclass classification of acute lymphoblastic leukemia microarrays data using support vector machine algorithms. J. Phys. Conf. Ser. 1490, 012027 (2020). https://doi.org/10.1088/1742-6596/1490/1/012027

  27. Hayes, J., Peruzzi, P.P., Lawler, S.: MicroRNAs in cancer: biomarkers, functions and therapy. Trends Molecular Med. 20(8), 460–469 (2014). https://doi.org/10.1016/j.molmed.2014.06.005

  28. Kumar, D., et al.: Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access 8, 142521–142531 (2020). https://doi.org/10.1109/ACCESS.2020.3012292

  29. Kumar, S., Mishra, S., Asthana, P., Pragya: Automated detection of acute leukemia using K-mean clustering algorithm. In: Automated Detection of Acute Leukemia using K-mean Clustering Algorithm, pp. 655–670 (2018). https://doi.org/10.1007/978-981-10-3773-_64

  30. Loey, M., Naman, M., Zayed, H.: Deep transfer learning in diagnosing leukemia in blood cells. Computers 9(2), 29 (2020). https://doi.org/10.3390/computers9020029

  31. Meijering, E.: Cell segmentation: 50 years down the road [life sciences]. IEEE Signal Process. Mag. 29(5), 140–145 (2012). https://doi.org/10.1109/MSP.2012.2204190

  32. Mirmohammadi, P., Ameri, M., Shalbaf, A.: Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier. Phys. Eng. Sci. Med. 44(2), 433–441 (2021). https://doi.org/10.1007/s13246-021-00993-5

  33. Mishra, S., Sharma, L., Majhi, B., Sa, P.K.: Microscopic image classification using DCT for the detection of acute lymphoblastic leukemia (ALL). In: Advances in Intelligent Systems and Computing, pp. 171–180 (2017). https://doi.org/10.1007/978-981-10-2104-6_16

  34. Purwanti, E., Calista, E.: Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features. J. Phys. Conf. Ser. 853, 012011 (2017). https://doi.org/10.1088/1742-6596/853/1/012011

  35. Ratley, A., Minj, J., Patre, P.: Leukemia disease detection and classification using machine learning approaches: a review. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), pp. 161–165. IEEE, January 2020. https://doi.org/10.1109/ICPC2T48082.2020.9071471

  36. Rengier, F., et al.: 3D printing based on imaging data: review of medical applications. Int. J. Comput. Assisted Radiol. Surg. 5(4), 335–341 (2010). https://doi.org/10.1007/s11548-010-0476-x

  37. Rovithakis, G., Maniadakis, M., Zervakis, M.: A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 34(1), 695–703 (2004). https://doi.org/10.1109/TSMCB.2003.811293

  38. Sahlol, A.T., Abdeldaim, A.M., Hassanien, A.E.: Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm. Soft Comput. 23(15), 6345–6360 (2019). https://doi.org/10.1007/s00500-018-3288-5

  39. Sajjad, M., et al.: Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access 5, 3475–3489 (2017). https://doi.org/10.1109/ACCESS.2016.2636218

  40. Selvaraj, S., Kanakaraj, B.: Naïve Bayesian classifier for acute lymphocytic leukemia detection. ARPN J. Eng. Appl. Sci. 10(16) (2015). http://www.arpnjournals.com/jeas/research_papers/rp_2015/jeas_0915_2495.pdf

  41. Shafique, S., Tehsin, S.: Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technol. Cancer Res. Treatment 17, 153303381880278 (2018). https://doi.org/10.1177/1533033818802789

  42. Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., Yang, L.: Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. Lecture Notes In Computer Science, pp. 383–390 (2015). https://doi.org/10.1007/978-3-319-24574-4_46

  43. Supardi, N.Z., Mashor, M.Y., Harun, N.H., Bakri, F.A., Hassan, R.: Classification of blasts in acute leukemia blood samples using k-nearest neighbour. In: 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, pp. 461–465, March 2012. https://doi.org/10.1109/CSPA.2012.6194769

  44. Ur Rahman, S.I., Jadoon, M., Ali, S., Khattak, H., Huang, J.: Efficient segmentation of lymphoblast in acute lymphocytic leukemia. Sci. Programm. 2021, 1–7 (2021). https://doi.org/10.1155/2021/7488025

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Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka, India

    Tulasi Gayatri Devi & Nagamma Patil

  2. Department of Pathology, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India

    Sharada Rai & Cheryl Sarah Philipose

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  1. Tulasi Gayatri Devi
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  2. Nagamma Patil
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  3. Sharada Rai
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  4. Cheryl Sarah Philipose
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Correspondence to Tulasi Gayatri Devi .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Devi, T.G., Patil, N., Rai, S., Philipose, C.S. (2022). Survey of Leukemia Cancer Cell Detection Using Image Processing. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_41

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  • DOI: https://doi.org/10.1007/978-3-031-11346-8_41

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  • Publisher Name: Springer, Cham

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