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  3. Vol. 10, No. 1, February 2025
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Vol. 10, No. 1, February 2025

Issue Published : Feb 1, 2025
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Detecting Acute Lymphoblastic Leukemia in Blood Smear Images using CNN and SVM

https://doi.org/10.22219/kinetik.v10i1.2027
Nelly Oktavia Adiwijaya
Universitas of Jember
Sultan Ardiansyah
University of Jember
Dwiretno Istiyadi Swasono
University of Jember

Corresponding Author(s) : Nelly Oktavia Adiwijaya

nelly.oa@unej.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 1, February 2025
Article Published : Feb 1, 2025

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Abstract

Acute Lymphoblastic Leukemia (ALL) is a common and aggressive subtype of leukemia that predominantly affects children. Accurate and timely diagnosis of ALL is critical for successful treatment, but it is hindered by the limitations of manual examination of peripheral blood smear images, which are prone to human error and inefficiency. This study proposes an improved diagnostic approach by integrating the EfficientNet architecture with a Support Vector Machine (SVM) classifier to enhance classification accuracy and address the performance inconsistencies of standalone EfficientNet models. Additionally, a novel CNN-based model with a reduced number of parameters is developed and evaluated. A dataset comprising 3.256 peripheral blood smear images across four classes (benign, early, pre and pro) was used for training and testing. The EfficientNet-SVM models achieved a peak accuracy of 97.35% using the EfficientNet-B3 architecture, surpassing previous studies. The improved CNN model achieved the highest accuracy of 99.18% while reducing parameters by 59.5% compared to the best prior models, with a negligible accuracy decrease of only 0.67%. These findings highlight the potential of combining EfficientNet with SVM and the efficiency of the improved CNN model for automated ALL detection, paving the way for more reliable, cost-effective, and scalable diagnostic tools.

Keywords

Convolutional Neural Network (CNN) Support Vector Machine (SVM) Peripheral Blood Smear Image Acute Lymphoblastic Leukemia EfficientNet
Adiwijaya, N. O., Ardiansyah, S., & Swasono, D. I. . (2025). Detecting Acute Lymphoblastic Leukemia in Blood Smear Images using CNN and SVM. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(1). https://doi.org/10.22219/kinetik.v10i1.2027
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References
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  35. K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908 LNCS, pp. 630–645, 2016. https://doi.org/10.1007/978-3-319-46493-0_38
  36. B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8697–8710, 2018. https://doi.org/10.1109/CVPR.2018.00907
  37. X. Zhang, Z. Li, C. C. Loy, and D. Lin, “PolyNet: A pursuit of structural diversity in very deep networks,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, no. 1, pp. 3900–3908, 2017. https://doi.org/10.1109/CVPR.2017.415
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References


P. K. Das and S. Meher, “An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia,” Expert Syst Appl, vol. 183, no. April, p. 115311, 2021. https://doi.org/10.1016/j.eswa.2021.115311

R. Rompies, S. P. Amelia, and S. Gunawan, “Perubahan Status Gizi pada Anak dengan Leukemia Limfoblastik Akut Selama Terapi,” e-CliniC, vol. 8, no. 1, pp. 152–157, 2019. https://doi.org/10.35790/ecl.8.1.2020.28290

A. Miranda-Filho, M. Piñeros, J. Ferlay, I. Soerjomataram, A. Monnereau, and F. Bray, “Epidemiological patterns of leukaemia in 184 countries: a population-based study,” Lancet Haematol, vol. 5, no. 1, pp. e14–e24, 2018. https://doi.org/10.1016/s2352-3026(17)30232-6

K. Dese et al., “Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images,” Clin Lymphoma Myeloma Leuk, vol. 21, no. 11, pp. e903–e914, 2021. https://doi.org/10.1016/j.clml.2021.06.025

A. Abhishek, R. K. Jha, R. Sinha, and K. Jha, “Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques,” Biomed Signal Process Control, vol. 72, no. PB, p. 103341, 2022. https://doi.org/10.1016/j.bspc.2021.103341

C. C. Chang et al., “Clinical significance of smudge cells in peripheral blood smears in hematological malignancies and other diseases,” Asian Pacific Journal of Cancer Prevention, vol. 17, no. 4, pp. 1847–1850, 2016. https://doi.org/10.7314/apjcp.2016.17.4.1847

R. Sarki, K. Ahmed, H. Wang, Y. Zhang, and K. Wang, “Automated detection of COVID-19 through convolutional neural network using chest x-ray images,” PLoS One, vol. 17, no. 1 January, pp. 1–26, 2022. https://doi.org/10.1371/journal.pone.0262052

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electronic Markets, vol. 31, no. 3, pp. 685–695, 2021. https://doi.org/10.1007/s12525-021-00475-2

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778, 2016. https://doi.org/10.1109/CVPR.2016.90

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun ACM, vol. 60, no. 6, pp. 84–90, 2017. https://doi.org/10.1145/3065386

G. Marques, D. Agarwal, and I. de la Torre Díez, “Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network,” Applied Soft Computing Journal, vol. 96, p. 106691, 2020. https://doi.org/10.1016/j.asoc.2020.106691

K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” Nov. 2015. https://doi.org/10.48550/arXiv.1511.08458

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2323, 1998. https://doi.org/10.1109/5.726791

C. Szegedy et al., “Going deeper with convolutions,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June, pp. 1–9, 2015. https://doi.org/10.1109/CVPR.2015.7298594

V. T. Hoang and K. H. Jo, “Slice Operator for Efficient Convolutional Neural Network Architecture,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12034 LNAI, no. March, pp. 163–173, 2020. https://doi.org/10.1007/978-3-030-42058-1_14

M. Ghaderzadeh, M. Aria, A. Hosseini, F. Asadi, D. Bashash, and H. Abolghasemi, “A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images,” International Journal of Intelligent Systems, vol. 37, no. 8, pp. 5113–5133, 2022. https://doi.org/10.1002/int.22753

M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, pp. 10691–10700, 2019. https://doi.org/10.48550/arXiv.1905.11946

M. Mohammed, M. B. Khan, and E. B. M. Bashie, Machine learning: Algorithms and applications. CRC Press, 2016. https://doi.org/10.1201/9781315371658

M. Aria, M. Ghaderzadeh, D. Bashash, H. Abolghasemi, F. Asadi, and A. Hosseini, “Acute Lymphoblastic Leukemia (ALL) image dataset,” Kaggle. https://doi.org/10.1002/int.22753

A. Sall et al., “Smudge cells percentage on blood smear is a reliable prognostic marker in chronic lymphocytic leukemia,” Hematol Transfus Cell Ther, vol. 44, no. 1, pp. 63–69, 2022. https://doi.org/10.1016/j.htct.2021.04.002

S. P. Chantepie, E. Cornet, V. Salaün, and O. Reman, “Hematogones: An overview,” Nov. 2013. https://doi.org/10.1016/j.leukres.2013.07.024

B. Y. A. W. Harris, C. A. Pinkert, M. Crawford, W. Y. Langdon, R. L. Brinster, and J. M. Adams, “THE Et . -myc TRANSGENIC MOUSE A Model for High-incidence Spontaneous Lymphoma and Leukemia of Early B Cells Translocation of the c-myc protooncogene into or near one of the Ig loci is found in almost every case of Burkitt ’ s B cell lymphoma in man and e,” vol. 167, no. February, 1988.

J. H. Cho-Vega, L. J. Medeiros, V. G. Prieto, and F. Vega, “Leukemia cutis,” Am J Clin Pathol, vol. 129, no. 1, pp. 130–142, 2008. https://doi.org/10.1309/WYACYWF6NGM3WBRT

F. E. Bertrand, C. Vogtenhuber, N. Shah, and T. W. Lebien, “Pro-B-cell to pre-B-cell development in B-lineage acute lymphoblastic leukemia expressing the MLL/AF4 fusion protein,” Blood, vol. 98, no. 12, pp. 3398–3405, 2001. https://doi.org/10.1182/blood.v98.12.3398

M. Urashima et al., “Establishment of a Human pro-B Cell Line (JKB-1) and Its Differentiation of Preestablished Bone Marrow Stromal Cell Layer,” 1994.

M. Perez-Andres et al., “Human peripheral blood B-Cell compartments: A crossroad in B-cell traffic,” Cytometry B Clin Cytom, vol. 78, no. SUPPL. 1, pp. 47–60, 2010. https://doi.org/10.1002/cyto.b.20547

A. Maqsood, M. S. Farid, M. H. Khan, and M. Grzegorzek, “Deep malaria parasite detection in thin blood smear microscopic images,” Applied Sciences (Switzerland), vol. 11, no. 5, pp. 1–19, 2021. https://doi.org/10.3390/app11052284

V. Singh, M. Pencina, A. J. Einstein, J. X. Liang, D. S. Berman, and P. Slomka, “Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging,” Sci Rep, vol. 11, no. 1, pp. 1–8, 2021. https://doi.org/10.1038/s41598-021-93651-5

S. H. Abdulhussain, B. M. Mahmmod, M. A. Naser, M. Q. Alsabah, R. Ali, and S. A. R. Al-Haddad, “A robust handwritten numeral recognition using hybrid orthogonal polynomials and moments,” Sensors, vol. 21, no. 6, pp. 1–18, 2021. https://doi.org/10.3390/s21061999

E. F. Saraswita, “Akurasi Klasifikasi Citra Digital Scenes RGB Menggunakan Model K-Nearest Neighbor dan Naive Bayes,” Prosiding Annual Research Seminar, vol. 5, no. 1, pp. 978–979, 2019.

A. Howard et al., “Searching for mobileNetV3,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 1314–1324, 2019. https://doi.org/10.1109/ICCV.2019.00140

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–14, 2015.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1800–1807, 2017. https://doi.org/10.1109/CVPR.2017.195

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 2818–2826, 2016. https://doi.org/10.1109/CVPR.2016.308

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908 LNCS, pp. 630–645, 2016. https://doi.org/10.1007/978-3-319-46493-0_38

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8697–8710, 2018. https://doi.org/10.1109/CVPR.2018.00907

X. Zhang, Z. Li, C. C. Loy, and D. Lin, “PolyNet: A pursuit of structural diversity in very deep networks,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, no. 1, pp. 3900–3908, 2017. https://doi.org/10.1109/CVPR.2017.415

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017. https://doi.org/10.1109/CVPR.2017.243

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