Accurate Machine Learning Algorithms Based on Detection of Leukemia Disease: A Review

Authors

  • Revella E. A. Armya Information Technology, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq
  • Nawzat Sadiq Ahmed Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq

DOI:

https://doi.org/10.25007/ajnu.v12n3a1051

Abstract

Abstract — Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. As one of the important aspects of diagnosing leukemia and determining its progress is identifying malignant cells. This paper illustrates the discovery of leukemia and its four main types through machine learning algorithms, as it was found that Computer-Aided Diagnosis (CAD) has progressed rapidly over the past few years. To identify leukemia, multiple machines learning algorithms have been created for early detection. Leukemia is a condition synonymous with white blood cells (WBC) that affect the bone marrow and/or blood. The early, healthy, and reliable diagnosis of leukemia has a major role in treating patients and saving their lives. To define leukemia in relation to its subtypes, several methods have been developed. However, these approaches include improvements in efficiency, learning process, and performance. This research paper is explained to enhance and provide rapid and stable detection of leukemia. To facilitate real-time collaboration between patients and healthcare providers for leukemia research, early diagnosis, and treatment. Thus it can save patients and doctors time and money. While the use of machine learning algorithms has shown accurate results, it depends on the shape and size of the sample and the type of algorithm used to classify the subtypes of leukemia (leukemia).

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References

S. Kumar, S. Mishra, and P. Asthana, “Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm,” 2018.

D. Q. Zeebaree, H. Haron, and A. M. Abdulazeez, “Gene Selection and Classification of Microarray Data Using Convolutional Neural Network,” ICOASE 2018 - Int. Conf. Adv. Sci. Eng., pp. 145–150, 2018, doi: 10.1109/ICOASE.2018.8548836.

S. Shafique and S. Tehsin, “Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks,” vol. 17, pp. 1–7, 2018, doi: 10.1177/1533033818802789.

T. T. P. Thanh, C. Vununu, S. Atoev, S. Lee, and K. Kwon, “Leukemia Blood Cell Image Classification Using Convolutional Neural Network,” vol. 10, no. 2, 2018.

A. C. Study, “Segmentation of Leukemia Cells Using Clustering :,” vol. 10, no. 2, pp. 39–48, 2019, doi: 10.4018/IJSE.2019070103.

R. R. Choudhary, S. Sharma, and G. M. B, “Detection of Leukemia in Human Blood Samples Through Image Processing,” pp. 824–834, 2018, doi: 10.1007/978-981-10-8660-1.

P. Sachin and R. Y. Kumar, “Detection and Classification of Blood Cancer from Microscopic Cell Images Using SVM KNN and NN Classifier,” Int. J. Adv. Res., vol. 3, no. 6, pp. 315–324, 2017, [Online]. Available: www.ijariit.com.

A. Belhekar, Y. Bhelkar, K. Gagare, K. Rajeswari, R. Bedse, and M. Karthikeyan, “Analytics,” 2019.

H. S. C. R. K-means, “Leukemia Image Segmentation Using a Hybrid Clustering Algorithm,” pp. 1–22, 2020.

D. Q. Zeebaree, H. Haron, A. M. Abdulazeez, and D. A. Zebari, “Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer,” 2019 Int. Conf. Adv. Sci. Eng. ICOASE 2019, pp. 106–111, 2019, doi: 10.1109/ICOASE.2019.8723827.

N. S. Ahmed, “A Fractal-Based Model To Improve Cooperation Among Physicians In Distributed Healthcare Information Systems.,” 2013.

N. Najat and A. M. Abdulazeez, “Gene clustering with partition around mediods algorithm based on weighted and normalized mahalanobis distance,” ICIIBMS 2017 - 2nd Int. Conf. Intell. Informatics Biomed. Sci., vol. 2018-Janua, pp. 140–145, 2018, doi: 10.1109/ICIIBMS.2017.8279707.

R. Arora and B. Arora, “Acute leukemia in children: A review of the current Indian data,” South Asian J. Cancer, vol. 5, no. 3, p. 155, 2016, doi: 10.4103/2278-330x.187591.

N. Bibi, M. Sikandar, I. U. Din, A. Almogren, and S. Ali, “IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning,” vol. 2020, 2020.

C. Lymphoma, S. No, and I. Erimbetova, “Abstracts from Proceedings of the Society of Hematologic Oncology 2020 Annual Meeting Rare Case o f Chr o nic Myel o id Leukemia Sec o ndary to Acu t e Lymph o blas t ic Leukemia in a Y o ung Adul t . A Case Rep o r t t he P o pula t i o n o f t he Republ,” vol. 20, no. September, pp. 159–172, 2020, doi: 10.1016/S2152-2650(20)30481-X.

D. A. Zebari, D. Q. Zeebaree, A. M. Abdulazeez, H. Haron, and H. N. A. Hamed, “Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images,” IEEE Access, vol. 8, pp. 203097–203116, 2020, doi: 10.1109/access.2020.3036072.

N. S. Ahmed and N. M. Yasin, “Inspiring a fractal approach in distributed healthcare information systems: A review,” Int. J. Phys. Sci., vol. 5, no. 11, pp. 1626–1640, 2010.

P. M. Gumble, “Analysis & Classification of Acute Lymphoblastic Leukemia using KNN Algorithm,” pp. 94–98, 2017.

T. Engineering, “Acute Leukemia Classification by Using SVM and K-Means Clustering,” pp. 1–4, 2014.

I. J. Maria, T. Devi, and D. Ravi, “Machine Learning Algorithms For Diagnosis Of Leukemia,” vol. 9, no. 01, pp. 267–270, 2020.

U. N. Wisesty, R. S. Warastri, and S. Y. Puspitasari, “Detection of acute lymphocyte leukemia using k- nearest neighbor algorithm based on shape and histogram features Detection of acute lymphocyte leukemia using k-nearest neighbor algorithm based on shape and histogram features.”

“Acute Lymphoblastic Leukemia Detection using Convolutional Neural Network,” vol. 10, no. 6, pp. 26529–26531, 2020.

A. Rehman, N. Abbas, and T. Saba, “Classification of acute lymphoblastic leukemia using deep learning,” no. September, pp. 1–8, 2018, doi: 10.1002/jemt.23139.

B. K. Das and H. S. Dutta, “GFNB : Gini index – based Fuzzy Naive Bayes and blast cell segmentation for leukemia detection using multi-cell blood smear images,” 2020.

R. S. Muthalaly, “USING DEEP LEARNING TO PREDICT THE MORTALITY OF,” 2017.

A. Gupta, R. Gupta, and S. Proceedings, ISBI 2019 C-NMC Challenge : Classification in Cancer Cell Imaging. 2019.

O. Ahmed and A. Brifcani, “Gene Expression Classification Based on Deep Learning,” 4th Sci. Int. Conf. Najaf, SICN 2019, pp. 145–149, 2019, doi: 10.1109/SICN47020.2019.9019357.

T. Hazra, M. Kumar, and S. S. Tripathy, “Automatic Leukemia Detection Using Image Processing Technique,” vol. VI, no. Iv, pp. 42–45, 2017.

D. Q. Zeebaree, H. Haron, A. M. Abdulazeez, and S. R. M. Zeebaree, “Combination of k-means clustering with genetic algorithm: A review,” Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 14238–14245, 2017.

N. S. Ahmed and M. Hikmat Sadiq, “Clarify of the Random Forest Algorithm in an Educational Field,” ICOASE 2018 - Int. Conf. Adv. Sci. Eng., pp. 179–184, 2018, doi: 10.1109/ICOASE.2018.8548804.

R. D. Souza and R. Fernandes, “LEUKEMIA PREDICTION USING RANDOM FOREST ALGORITHM,” vol. 8, no. 3, pp. 1–8, 2018.

L. H. S. Vogado, R. M. S. Veras, F. H. D. Araujo, R. R. V Silva, and R. T. Aires, “Engineering Applications of Artificial Intelligence Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification,” Eng. Appl. Artif. Intell., vol. 72, no. October 2017, pp. 415–422, 2018, doi: 10.1016/j.engappai.2018.04.024.

D. Umamaheswari and S. Geetha, Review on image segmentation techniques incorporated with machine learning in the scrutinization of leukemic microscopic stained blood smear images, vol. 30. Springer International Publishing, 2019.

R. Bhukya, B. Prasanth, V. S. Vihari, and Y. Ajay, “International Journal of Advanced and Applied Sciences Detection of acute lymphoblastic leukemia using microscopic images of blood,” vol. 4, no. 8, pp. 74–78, 2017.

“North Dakota State University,” no. May, 2019.

Published

2023-08-02

How to Cite

E. A. Armya, R., & Sadiq Ahmed, N. (2023). Accurate Machine Learning Algorithms Based on Detection of Leukemia Disease: A Review. Academic Journal of Nawroz University, 12(3), 281–288. https://doi.org/10.25007/ajnu.v12n3a1051

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Articles