2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 9 , Issue 1 , PP: 70-76, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Devanshu Joshi 1 * , Rishabh Tater 2 , Priya Yaday 3 , Tripti Jain 4 , Preeti Nagrath 5
Doi: https://doi.org/10.54216/FPA.090106
Leukemia is a type of blood cancer. Leukemia is cancer that begins in the blood cells. The lymphocytes and other blood cells are created in the bone marrow. When a person has leukemia the bone marrow does not function properly. Leukemia cells are produced by the bone marrow. Leukemia cells are mainly referred to as "rupture". These naive cancer cells block the cells that create the bone marrow. In this paper, various approaches to the classification & automatic detection of leukemia are described. The experiment was successfully implemented in Kaggle. Deep Learning algorithms were largely used in the treatment of Leukemia for the classification & detection of its presence in a patient. The paper describes Convolutional Neural Networks (CNN) and Visual Geometry Group-16(VGG-16) algorithms that are used to categorize leukemia into its sub-types and presents a comprehensive study of these algorithms.
Leukemia , Cancer , WBC , Convolutional Neural Networks , Visual Geometry Group-16 , AML , Deep Learning
[1] Maria, I. J., Devi, T., & Ravi, D. (2020). Machine learning algorithms for the diagnosis of leukemia. Int J Sci Technol Res, 9(1).
[2] Vaghela, H., Modi, H., Pandya, M., & Potdar, M. B. (2016). A novel approach to detect chronic leukemia using shape-based feature extraction and identification with digital image processing. International Journal of Applied Information Systems (IJAIS), 11(5), 9-16.
[3] Janaki, R. (2020). Detection of leukemia in microscopic white blood cell images using Gaussian feature convolutional visual recognition algorithm. J Critic Rev, 7(3), 173-180.
[4] Bibi, N., Sikandar, M., Ud Din, I., Almogren, A., & Ali, S. (2020). IoMT-based automated detection and classification of leukemia using deep learning. Journal of healthcare engineering, 2020.
[5] Sahlol, A. T., Kollmannsberger, P., & Ewees, A. A. (2020). Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Scientific Reports, 10(1), 1-11.
[6] Salah, H. T., Muhsen, I. N., Salama, M. E., Owaidah, T., & Hashmi, S. K. (2019). Machine learning applications in the diagnosis of leukemia: Current trends and future directions. International journal of laboratory hematology, 41(6), 717-725.
[7] Kumar, D., Jain, N., Khurana, A., Mittal, S., Satapathy, S. C., Senkerik, R., & Hemanth, J. D. (2020). Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access, 8, 142521-142531.
[8] Muthumayil, K., Manikandan, S., Srinivasan, S., Escorcia-Gutierrez, J., Gamarra, M., & Mansour, R. F. (2021). Diagnosis of leukemia disease based on enhanced virtual neural network. Computers, Materials & Continua.
[9] Mohammed Shahnawaz, K., & Kumar Saini, M. (2019). Detection Of Leukemia Using Digital Image Processing And Artificial Intelligence. International Journal of VLSI Design and Technology, 1(2), 13-21.
[10] Raje, C., & Rangole, J. (2014, April). Detection of Leukemia in microscopic images using image processing. In 2014 International Conference on Communication and Signal Processing (pp. 255-259). IEEE.
[11] Ghaderzadeh, M., Asadi, F., Hosseini, A., Bashash, D., Abolghasemi, H., & Roshanpour, A. (2021).Machine learning in detection and classification of leukemia using smear blood images: a systematic review. Scientific Programming, 2021.
[12] Mondal, C., Hasan, M., Jawad, M., Dutta, A., Islam, M., Awal, M., & Ahmad, M. (2021). Acute Lymphoblastic Leukemia detection from microscopic images using weighted ensembles of convolutional neural networks. arXiv preprint arXiv:2105.03995.
[13] Ahmed, N., Yigit, A., Isik, Z., & Alpkoçak, A. (2019). Identification of leukemia subtypes from microscopic images using convolutional neural networks. Diagnostics, 9(3), 104.
[14] Shafique, S., & Tehsin, S. (2018). Computer-aided diagnosis of acute lymphoblastic leukemia. Computational and mathematical methods in medicine, 2018.
[15] Paswan, S., & Rathore, Y. K. (2017). Detection and classification of blood cancer from microscopic cell images using SVM KNN and NN classifiers. Int. J. Adv. Res. Ideas Innov. Technol, 3, 315-324.
[16] Billah, M. E., & Javed, F. (2022). Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer. Applied Artificial Intelligence, 1-22.
[17] Shafique, S., & Tehsin, S. (2018). Acute lymphoblastic leukemia detection and classification of its subtypes using pre-trained deep convolutional neural networks. Technology in cancer research & treatment, 17, 1533033818802789.
[18] Madanan, M., Venugopal, A., & Velayudhan, N. C. (2020, July). Designing an artificial intelligence model using machine learning algorithms and applying it to hematology for the detection and classification of various stages of blood cancer. In Int. Conf. Innovate. Tech. Adv. Dis. Manag.
[19] Rupapara, V., Rustam, F., Aljedaani, W., Shahzad, H. F., Lee, E., & Ashraf, I. (2022). Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model. Scientific reports, 12(1), 1-15.
[20] Saba, T. (2020). Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges. Journal of Infection and Public Health, 13(9), 1274-1289.
[21] Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019, October). A hybrid deep learning architecture for leukemic B-lymphoblast classification. In 2019 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 271-276). IEEE.
[22] Bukhari, M., Yasmin, S., Sammad, S., El-Latif, A., & Ahmed, A. (2022). A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning. Mathematical Problems in Engineering, 2022.
[23] Khan, S., Sajjad, M., Hussain, T., Ullah, A., & Imran, A. S. (2020). A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images. IEEE Access, 9, 10657-10673.
[24] Gupta, A., & Sharma, P. (2021). A Review of Machine Learning Techniques Being Used For Blood Cancer Detection. Annals of the Romanian Society for Cell Biology, 7796-7811
