Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
DOI:
https://doi.org/10.64296/vijir.v1i2.02Keywords:
Deep Learning, Brain Tumor, MRI, ClassificationAbstract
A mutation in the DNA of a single cell that compromises its function initiates leukemia. This leads to the overproduction of immature white blood cells, which encroach upon the space required for the generation of healthy blood cells. Leukemia is treatable if identified in its initial stages. Nonetheless, its diagnosis is both arduous and time-consuming. In this study, a novel approach for diagnosing leukemia across four stages—Benign, Early, Pre, and Pro—utilizing deep learning techniques. Firstly, we had employed two Convolutional Neural Network (CNN) models: MobileNetV2 with an altered head and a bespoke model. The custom model has multiple convolutional layers, each paired with corresponding max pooling layers. Nowwe have employed two more pretrained model: InceptionV2 and VGG16 with altered heads. We utilized MobileNetV2, InceptionV2 and VGG16 with ImageNet weights, and the head was adjusted to integrate the final results. Finally, we have utilized ensemble with soft voting technique to comprehend the results from multiple neural networks. The utilized dataset is a publicly available collection of blood cell smear images titled “Acute Lymphoblastic Leukemia (ALL) image dataset”, and then used the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset. Which attained an accuracy of 96.34% with the custom model, while MobileNetV2 and InceptionV2 achieved a superior accuracy of 99.39%. The VGG16 achieved an accuracy of 99.08%. Finally, the ensemble technique ensured more promising result with 99.70% accuracy. The pre-trained model exhibited encouraging results and an increased likelihood of real-world application.
References
[1] D. Goutam and S. Sailaja, “Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier,” in ICETECH 2015- 2015 IEEE International Conference on Engineering and Technology, 2015.
[2] R. Ahmad, M. Awais, N. Kausar, and T. Akram, “White blood cells classification using entropy-controlled deep features optimization,” Diagnostics, vol. 13, no. 3, 2023.
[3] S. Mishra, B. Majhi, and P. K. Sa, “Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection,” Biomed Signal Process Control, vol. 47, 2019.
[4] M. Sheykhhasan, H. Manoochehri, and P. Dama, “Use of car t-cell for acute lymphoblastic leukemia (all) treatment: a review study,” 2022.
[5] R. H. Rifat, M. S. Poran, S. Islam, A. T. Sumaya, M. M. Alam, and M. R. Rahman, “Incidence, mortality, and epidemiology of leukemia in south asia: An ecological study,” Open J Epidemiol, vol. 13, no. 01, 2023.
[6] R. T. Raphael and K. R. Joy, “Segmentation and classification techniques of leukemia using image processing: An overview,” in Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2019, 2019.
[7] R. Zemouri, N. Zerhouni, and D. Racoceanu, “Deep learning in the biomedical applications: Recent and future status,” Applied Sciences (Switzerland), vol. 9, no. 8, 2019.
[8] M. J. Ferdous, D. M. Asadujjaman, and M. M. Rahman, “An ensemble deep transfer learning approach for classifying COVID-19, pneumonia, and healthy lungs in chest x-ray images with GRAD-CAM visualization,” in 2024 27th International Conference on Computer and Information Technology (ICCIT), pp. 3075–3080, IEEE, 2024.
[9] M. M. Ikbal, D. M. Asadujjaman, M. M. Rahman, A. Tamim, M. M. Rahman, M. S. Shakil, M. Z. Hasan, and S. Yasmin, “Intelligent recognition of bangla handwritten digits,” in 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), pp. 1–6, IEEE, 2025.
[10] M. Z. Hasan, A. Tamim, D. M. Asadujjaman, M. M. Rahman, M. A. A. Mollick, N. A. Dristi, and A. Al Noman, “A CNN approach to automated detection and classification of brain tumors,” in 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6, IEEE, 2025.
[11] F. Ahmmed, M. Z. Raihan, K. Nahar, and D. M. Asadujjaman, “Automated detection and classification method of brain tumors using CNNs and deep transfer learning,” in 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), pp. 1–6, IEEE, 2025.
[12] C. Mitra, M. M. Ikbal, F. Parvin, M. M. Rahman, and D. M. Asadujjaman, “Transfer learning based multiclass brain tumor classification using MRI data,” in 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), pp. 1–6, IEEE, 2025.
[13] M. M. Rahman, D. M. Asadujjaman, M. M. Ikbal, S. Shakil, A. Tamim, M. Rahman, and S. Yasmin, “Recognizing bangla numerals: A deep learning approach on a novel handwritten dataset,” in Lecture Notes in Networks and Systems, pp. 100–111, Cham: Springer Nature Switzerland, 2026.
[14] D. M. Asadujjaman and P. C. Shill, “Cardiovascular disease diagnosis prediction system using machine learning,” in Smart Innovation, Systems and Technologies, pp. 309–319, Singapore: Springer Nature Singapore, 2024.
[15] A. Tamim, M. W. Jahan, M. R. S. Chowdhury, A. Hossain, M. M. Rahman, and A. H. M. R. Imon, “Machine learning-driven market value prediction for european football players,” J. Comput. Math. Data Sci., vol. 15, no. 100118, p. 100118, 2025.
[16] M. Bukhari, S. Yasmin, S. Sammad, and A. A. A. El-Latif, “A deep learning framework for leukemia cancer detection in microscopic blood samples using squeeze and excitation learning,” Math Probl Eng, vol. 2022, pp. 1–18, 2022.
[17] D. J. Foran, D. Comaniciu, P. Meer, and L. A. Goodell, “Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy,” IEEE Transactions on Information Technology in Biomedicine, vol. 4, no. 4, 2000.
[18] S. Dabeer, M. M. Khan, and S. Islam, “Cancer diagnosis in histopathological image: Cnn based approach,” Inform Med Unlocked, vol. 16, 2019.
[19] N. Ahmed, A. Yigit, Z. Isik, and A. Alpkocak, “Identification of leukemia subtypes from microscopic images using convolutional neural network,” Diagnostics, vol. 9, no. 3, 2019.
[20] A. Rehman, N. Abbas, T. Saba, S. I. ur Rahman, Z. Mehmood, and H. Kolivand, “Classification of acute lymphoblastic leukemia using deep learning,” Microsc Res Tech, vol. 81, no. 11, 2018.
[21] L. Boldú, A. Merino, A. Acevedo, A. Molina, and J. Rodellar, “A deep learning model (alnet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images,” Comput Methods Programs Biomed, vol. 202, 2021.
[22] L. Ma, R. Shuai, X. Ran, W. Liu, and C. Ye, “Combining dc-gan with resnet for blood cell image classification,” Med Biol Eng Comput, vol. 58, no. 6, 2020.
[23] R. B. Hegde, K. Prasad, H. Hebbar, B. M. K. Singh, and I. Sandhya, “Automated decision support system for detection of leukemia from peripheral blood smear images,” J Digit Imaging, vol. 33, no. 2, 2020.
[24] S. Dasariraju, M. Huo, and S. McCalla, “Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm,” Bioengineering, vol. 7, no. 4, 2020.
[25] S. Ramaneswaran, K. Srinivasan, P. M. D. R. Vincent, and C. Y. Chang, “Hybrid inception v3 xgboost model for acute lymphoblastic leukemia classification,” Journal of Healthcare Engineering, 2021.
[26] N. Sampathila et al., “Customized deep learning classifier for detection of acute lymphoblastic leukemia using blood smear images,” Healthcare (Switzerland), vol. 10, no. 10, 2022.
[27] A. Sulaiman et al., “Resrandsvm: Hybrid approach for acute lymphocytic leukemia classification in blood smear images,” Diagnostics, vol. 13, no. 12, 2023.
[28] O. S. Ghongade, S. K. S. Reddy, Y. C. Gavini, S. Tokala, and M. K. Enduri, “Acute lymphoblastic leukemia blood cells prediction using deep learning transfer learning technique,” Indonesian Journal of Electrical Engineering and Informatics, vol. 11, no. 3, 2023.
[29] M. Aria, M. Ghaderzadeh, D. Bashash, H. Abolghasemi, F. Asadi, and A. Hosseini, “Acute lymphoblastic leukemia (all) image dataset.” [https://www.kaggle.com/](https://www.kaggle.com/). Accessed on: August 2025.
[30] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018.
