Integrating Multiclass Classifiers For Enhanced Acute Lymphoblast Leukemia Detection: A Comparative Study
Abstract
Acute lymphoblastic leukemia (ALL) is a blood and bone marrow malignancy that is characterized by the growth of many immature lymphocytes known as lymphoblasts. It primarily affects children, particularly those aged two to five years, and is the primary cause of death in pediatric cancer cases. The method of treatment is determined to ALL, the individual’s age at the time of diagnosis, and other pertinent considerations. Regardless, early detection and diagnosis are critical for a good prognosis. It is critical to precisely detect malignant cells to make a diagnosis and assess the extent of the disease. However, due to physical similarities, identifying lymphoblasts from normal white blood cells under a microscope is often difficult. Using computeraided techniques can be extremely valuable in automating the identification of cancerous cells, allowing histopathologists and oncologists to make decisions about the early stage. This paper demonstrates the usefulness of extensive image pre-processing, feature extraction from ResNet50 and VGG19 CNN models, and robust feature selection in an automated diagnostic technique for Acute Lymphoblastic Leukemia. Notably, on the CNMC 2019 Dataset, ResNet50 with Random Forest feature selection appears as the best combination. The ResNet50 model achieves maximal precision, Weighted F1 Score, F1-score accuracy, and recall of 84.18%, 80.4%, 86.15%, 80.83%, and 88.7% respectively when combined with ANOVA and Random Forest. The combination of VGG19+Random Forest+SVM achieves a maximum accuracy of 86.2%. These findings highlight its exceptional performance in recognizing and categorizing target labels, demonstrating its ability to extract relevant properties for improved leukemia identification.
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National Cancer Institute, "Cancer Stat Facts: Leukemia — Acute Lymphocytic Leukemia (ALL)," 2020. [Online]. Available: https://seer.cancer.gov/statfacts/html/alyl.html. Accessed: Feb. 13, 2025.
W. Ladines-Castro, F.J. González-Castro, A.L. Fernández-Figueroa, E.O. Rodríguez-Pérez, L.M. Sánchez-Corona, and M.A. Chacón-Rodríguez, "Morphology of leukaemias," Revista Médica del Hospital General de México, vol. 79, no. 2, pp. 107–113, 2016. doi: 10.1016/j.hgmx.2015.06.007.
R. B. Hegde, K. Prasad, H. Hebbar, and B. M. K. Singh, "Image processing approach for detection of leukocytes in peripheral blood smears," Journal of Medical Systems, vol. 43, no. 5, 2019. doi: 10.1007/s10916-019-1219-3.
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P. K. Das, A. Pradhan, and S. Meher, "Detection of acute lymphoblastic leukemia using machine learning techniques," in Machine Learning, Deep Learning, and Computational Intelligence for Wireless Communication: Proceedings of MDCWC 2020, 2021, pp. 425–437.
S. Mandal, V. Daivajna, and V. Rajagopalan, "Machine learning-based system for automatic detection of leukemia cancer cell," in Proceedings of the 2019 IEEE 16th India Council International Conference (INDICON), 2019, pp. 1–4.
I. Alrashdi and A. Alqazzaz, "Synergizing AI, IoT, and blockchain for diagnosing pandemic diseases in smart cities: Challenges and opportunities," Sustainable Machine Intelligence Journal, vol. 7, pp. 1–28, 2024. doi: 10.61356/smij.2024.77106.
N. Khalil, M. Elkholy, and M. Eassa, "A comparative analysis of machine learning models for prediction of chronic kidney disease," Sustainable Machine Intelligence Journal, vol. 5, pp. 1–3, 2023.
Q. Wang, Y. Xie, M. Wang, H. Zhou, Y. Xu, L. Liu, and Z. Zhang, "Deep learning approach to peripheral leukocyte recognition," PLoS One, vol. 14, no. 6, pp. 1–18, 2018. doi: 10.1371/journal.pone.0218808.
S. Shafique and S. Tehsin, "Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks," Technology in Cancer Research & Treatment, vol. 17, pp. 1–7, 2018. doi: 10.1177/1533033818802789.
K. K. Anilkumar, V. J. Manoj, and T. M. Sagi, "Automated detection of B cell and T cell acute lymphoblastic leukaemia using deep learning," IRBM (Institut de Recherche Biomédicale), vol. 43, no. 5, pp. 405–413, 2022. doi: 10.1016/j.irbm.2021.05.005.
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M. Zhou et al., "Development and evaluation of a leukemia diagnosis system using deep learning in real clinical scenarios," Frontiers in Pediatrics, vol. 9, no. June, pp. 1–10, 2021. doi: 10.3389/fped.2021.693676.
A. Akter et al., "Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor," Expert Systems with Applications, vol. 238, no. PF, p. 122347, 2024. doi: 10.1016/j.eswa.2023.122347.
R. Nayak, A. Bekal, M. Suvarna, and D. Sathish, "Identifying subtypes of acute lymphoblastic leukemia using blood smear images: A hybrid learning approach," Journal of The Institution of Engineers (India): Series B, 2024. doi: 10.1007/s40031-024-01069-0.
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A. Gupta et al., "GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images," Medical Image Analysis, vol. 65, p. 101788, 2020. doi: 10.1016/j.media.2020.101788.
R. Gupta, P. Mallick, R. Duggal, A. Gupta, and O. Sharma, "Stain color normalization and segmentation of plasma cells in microscopic images as a prelude to development of computerassisted automated disease diagnostic tool in multiple myeloma," Clinical Lymphoma, Myeloma & Leukemia, vol. 17, no. 1, p. e99, 2017.
R. Duggal, A. Gupta, R. Gupta, M. Wadhwa, and C. Ahuja, "Overlapping cell nuclei segmentation in microscopic images using deep belief networks," in Proceedings of the 10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), 2016, pp. 1–8.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.
K. Simonyan, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
R. K. Sachdeva et al., "A systematic method for breast cancer classification using RFE feature selection," in Proceedings of the 2022 2nd International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 1673–1676. doi: 10.1109/ICACITE53722.2022.9823464.
M. N. Triba et al., "PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters." Molecular BioSystems, vol. 11, no. 1 13-19, 2015.
W. H. Abir, M. F. Uddin, F. R. Khanam, and M. M. Khan, "Explainable AI in diagnosing and anticipating leukemia using transfer learning method," arXiv e-prints, p. arXiv--2312, 2023.
I. Q. Khilji, K. Saha, J. A. Shonon, and M. I. Hossain, "Application of homomorphic encryption on neural network in prediction of acute lymphoid leukemia," International Journal of Advanced Computer Science and Applications, vol. 11, no. 6, pp. 350–360, 2020. doi: 10.14569/IJACSA.2020.0110646.
Y. Ding, Y. Yang, and Y. Cui, "Deep learning for classifying white blood cancer," in ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging: Select Proceedings, Springer, 2019, pp. 33–41.
L. Boldú, A. Merino, S. Alférez, A. Molina, A. Acevedo, and J. Rodellar, "Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis," Journal of Clinical Pathology, vol. 72, no. 11, pp. 755–761, 2019.
Published
2025-04-08
How to Cite
NAYAK, Roopashree; BEKAL, Anush; SALDANHA, Jennifer C.
Integrating Multiclass Classifiers For Enhanced Acute Lymphoblast Leukemia Detection: A Comparative Study.
Yugoslav Journal of Operations Research, [S.l.], apr. 2025.
ISSN 2334-6043.
Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/1333>. Date accessed: 14 apr. 2025.
doi: https://doi.org/10.2298/YJOR240315006N.
Issue
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
