Fusi Cross-Attention CNN–Transformer untuk Klasifikasi Multi-Kelas Acute Lymphoblastic Leukemia
-
Wahyuni Fithratul Zalmi
*
Universitas Sam Ratulangi, Manado,
Indonesia
- Rahmi Putri Kurnia Politeknik Negeri Padang, Padang, Indonesia
- Yulia Jihan Sy Politeknik Negeri Padang, Padang, Indonesia (*) Corresponding Author
Abstract
Acute Lymphoblastic Leukemia (ALL) is a hematological malignancy that requires a quick and accurate initial examination. Peripheral Blood Smear (PBS) imaging can be used as a source of cell morphology information, but image-based classification still faces challenges due to variations in the shape, color, and structure of blood cells. This study proposes an ALL multi-class classification model based on CNN–Transformer cross-attention fusion with two image inputs, namely the original PBS image and the segmented image that is already available in the dataset. The main contribution of this study lies in the integration of the local features of the CNN and the global features of the Transformer through the cross-attention mechanism, as well as the evaluation of the model components through baseline comparison and ablation studies. The dataset used consisted of 3,256 pairs of PBS images in four classes, namely Benign, Early, Pre, and Pro. The test results showed that the model obtained an accuracy of 0.9980 and a macro F1-score of 0.9975 on the test data. Nonetheless, this very high performance needs to be interpreted with caution as the research has not involved external validation based on different institutions or direct assessments by pathologists. Therefore, the proposed model is more appropriately positioned as a potential computational approach to the classification of PBS images, rather than as a final clinical diagnostic system. Advanced evaluation of external datasets, patient-based allocation schemes, and expert validation are required to assess the generalization and clinical relevance of the model.
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References
A. Krętowska-Grunwald et al., “Significance of Th17 and Treg in Treatment Efficacy and Outcome in Pediatric Acute Lymphoblastic Leukemia,” Int. J. Mol. Sci., vol. 24, no. 15, p. 12323, Aug. 2023, doi: 10.3390/ijms241512323.
E. Kassa et al., “Clinical profile and treatment outcomes of acute lymphoblastic leukemia among children attending at the University of Gondar comprehensive specialized hospital and Tikur Anbessa Specialized Hospital in Ethiopia,” PLOS One, vol. 20, no. 6, p. e0322747, Jun. 2025, doi: 10.1371/journal.pone.0322747.
P. K. Das, D. V. A, S. Meher, R. Panda, and A. Abraham, “A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia,” IEEE Access, vol. 10, pp. 81741–81763, 2022, doi: 10.1109/ACCESS.2022.3196037.
I. Ahmad et al., “Pediatric Acute Lymphoblastic Leukemia: Clinical Characteristics, Treatment Outcomes, and Prognostic Factors: 10 Years’ Experience From a Low- and Middle-Income Country,” JCO Glob. Oncol., no. 9, p. e2200288, Jun. 2023, doi: 10.1200/GO.22.00288.
A. D. Saucedo-Campos et al., “Peripheral Blood as a Diagnostic Alternative to Bone Marrow in Immunophenotyping Pediatric B-Cell Acute Lymphoblastic Leukemia,” Int. J. Mol. Sci., vol. 27, no. 1, p. 193, Dec. 2025, doi: 10.3390/ijms27010193.
K. S. Al-Badrani et al., “Immunophenotyping in diagnosing pediatric acute leukemia after setting up the first flow cytometry unit in Mosul City in Iraq: an observational study of the project performed through a contribution from Japan,” Transl. Pediatr., vol. 14, no. 5, pp. 900–914, May 2025, doi: 10.21037/tp-2025-24.
R. F. Oybek Kizi, T. P. Theodore Armand, and H.-C. Kim, “A Review of Deep Learning Techniques for Leukemia Cancer Classification Based on Blood Smear Images,” Appl. Biosci., vol. 4, no. 1, p. 9, Feb. 2025, doi: 10.3390/applbiosci4010009.
T. Mustaqim, C. Fatichah, and N. Suciati, “Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 16108–16127, 2023, doi: 10.1109/ACCESS.2023.3245128.
G. Papanastasiou, N. Dikaios, J. Huang, C. Wang, and G. Yang, “Is Attention all You Need in Medical Image Analysis? A Review,” IEEE J. Biomed. Health Inform., vol. 28, no. 3, pp. 1398–1411, Mar. 2024, doi: 10.1109/JBHI.2023.3348436.
S. Takahashi et al., “Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review,” J. Med. Syst., vol. 48, no. 1, p. 84, Sep. 2024, doi: 10.1007/s10916-024-02105-8.
F. A. Mohammed, K. K. Tune, B. G. Assefa, M. Jett, and S. Muhie, “Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature,” Mach. Learn. Knowl. Extr., vol. 6, no. 1, pp. 699–736, Mar. 2024, doi: 10.3390/make6010033.
R. F. Oybek Kizi, T. P. Theodore Armand, and H.-C. Kim, “A Review of Deep Learning Techniques for Leukemia Cancer Classification Based on Blood Smear Images,” Appl. Biosci., vol. 4, no. 1, p. 9, Feb. 2025, doi: 10.3390/applbiosci4010009.
R. Azad et al., “Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review,” 2023, arXiv. doi: 10.48550/ARXIV.2301.03505.
G. Papanastasiou, N. Dikaios, J. Huang, C. Wang, and G. Yang, “Is Attention all You Need in Medical Image Analysis? A Review,” IEEE J. Biomed. Health Inform., vol. 28, no. 3, pp. 1398–1411, Mar. 2024, doi: 10.1109/JBHI.2023.3348436.
M. Aamir, Z. Rahman, N. Choudhry, J. Ahmed Bhutto, W. Ahmed Abro, and Z. Zhu, “From CNNs to Transformers: A Review of Evolving Deep Learning Architectures for Brain Tumor Classification,” IEEE Access, vol. 13, pp. 184918–184936, 2025, doi: 10.1109/ACCESS.2025.3625607.
X. Jiang, Z. Hu, S. Wang, and Y. Zhang, “Deep Learning for Medical Image-Based Cancer Diagnosis,” Cancers, vol. 15, no. 14, p. 3608, Jul. 2023, doi: 10.3390/cancers15143608.
Y. Li et al., “A review of deep learning-based information fusion techniques for multimodal medical image classification,” Comput. Biol. Med., vol. 177, p. 108635, Jul. 2024, doi: 10.1016/j.compbiomed.2024.108635.
H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt, “Transfer learning for medical image classification: a literature review,” BMC Med. Imaging, vol. 22, no. 1, p. 69, Dec. 2022, doi: 10.1186/s12880-022-00793-7.
K. He et al., “Transformers in medical image analysis,” Intell. Med., vol. 3, no. 1, pp. 59–78, Feb. 2023, doi: 10.1016/j.imed.2022.07.002.
Y. Li et al., “A review of deep learning-based information fusion techniques for multimodal medical image classification,” Comput. Biol. Med., vol. 177, p. 108635, Jul. 2024, doi: 10.1016/j.compbiomed.2024.108635.
G. Papanastasiou, N. Dikaios, J. Huang, C. Wang, and G. Yang, “Is Attention all You Need in Medical Image Analysis? A Review,” IEEE J. Biomed. Health Inform., vol. 28, no. 3, pp. 1398–1411, Mar. 2024, doi: 10.1109/JBHI.2023.3348436.
T. Mustaqim, C. Fatichah, and N. Suciati, “Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 16108–16127, 2023, doi: 10.1109/ACCESS.2023.3245128.
M. Shahzad et al., “Blood cell image segmentation and classification: a systematic review,” PeerJ Comput. Sci., vol. 10, p. e1813, Feb. 2024, doi: 10.7717/peerj-cs.1813.
Y. Li et al., “A review of deep learning-based information fusion techniques for multimodal medical image classification,” Comput. Biol. Med., vol. 177, p. 108635, Jul. 2024, doi: 10.1016/j.compbiomed.2024.108635.
K. He et al., “Transformers in medical image analysis,” Intell. Med., vol. 3, no. 1, pp. 59–78, Feb. 2023, doi: 10.1016/j.imed.2022.07.002.
S. Q. Alhamrani et al., “Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review,” Cells, vol. 14, no. 17, p. 1385, Sep. 2025, doi: 10.3390/cells14171385.
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