Multi-Class Classification of Immature Leukocytes in AML Blood Smears Using an Ensemble of CNNs
Resumo
A Leucemia Mieloide Aguda (LMA) é uma neoplasia caracterizada pela proliferação anormal de leucócitos imaturos, cuja identificação é essencial para o diagnóstico. Este estudo avaliou o uso de Redes Neurais Convolucionais (CNNs) na classificação automática de cinco subtipos celulares em esfregaços sanguíneos. Oito arquiteturas pré-treinadas foram testadas com aumento de dados, validação cruzada e aprendizado por transferência. A partir das quatro melhores redes foram formados quatro comitês por votação majoritária, sendo que o mais eficaz alcançou acurácia de 88,49% e superou os modelos individuais. Os resultados indicam o potencial da abordagem para auxiliar o diagnóstico da LMA.Referências
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Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312.
Thanh, T. T. P., Vununu, C., Atoev, S., Lee, S.-W., and Kwon, K.-R. (2018). Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10:54–58.
Vogado, L. H. S., Veras, R. M. S., and Aires, K. R. T. (2021). Leuknet: Um modelo de rede neural convolucional para o diagnóstico de leucemia. In Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 85–90. Sociedade Brasileira de Computação.
Yamamoto, J. F. and Goodman, M. T. (2008). Patterns of leukemia incidence in the united states by subtype and demographic characteristics, 1997–2002. Cancer Causes & Control, 19(4):379–390.
American Cancer Society (2025). Key statistics for acute myeloid leukemia (aml). [link]. Accessed on: 29 Mar. 2025.
Berrar, D. (2019). Cross-validation. In Ranganathan, S., Gribskov, M., Nakai, K., and Schönbach, C., editors, Encyclopedia of Bioinformatics and Computational Biology, pages 542–545. Academic Press, Oxford.
Boldú, L., Merino, A., Acevedo, A., Molina, A., and Rodellar, J. (2021). A deep learning model (alnet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. Computer Methods and Programs in Biomedicine, 202:105999.
Chennamadhavuni, A., Iyengar, V., Mukkamala, S. K. R., and Shimanovsky, A. (2023). Leukemia. StatPearls [Internet]. Available at: [link].
Dasariraju, S., Huo, M., and McCalla, S. (2020). Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm. Bioengineering.
Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems, pages 1–15. Springer.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Matek, C., Schwarz, S., Spiekermann, K., and Marr, C. (2019). Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nature Machine Intelligence, 1(11):538–544.
Mumuni, A.-R. and Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 16:100258.
Powers, D. M. W. (2020). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation.
Rahman, J. and Ahmad, M. (2023). Detection of acute myeloid leukemia from peripheral blood smear images using transfer learning in modified cnn architectures. pages 447–459.
Rosenfield, G. H. and Fitzpatrick-Lins, K. (1986). A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing.
Santos, M., Bertemes, W., de Souza, , Andrades, M., Barros, D., and Patto, V. (2022). Algoritmos de aprendizado de máquina para classificação de células nucleadas do sangue periférico - uma experiência do projeto hemovision. In Anais da X Escola Regional de Informática de Goiás, pages 130–140. SBC.
Saultz, J. N. and Garzon, R. (2016). Acute myeloid leukemia: a concise review. Journal of Clinical Medicine, 5(3):33.
Sidhom, J.-W., Siddarthan, I. J., Lai, B. S., Luo, A., Hambley, B. C., Bynum, J., Duffield, A. S., Streiff, M. B., Moliterno, A. R., Imus, P., et al. (2021). Deep learning for diagnosis of acute promyelocytic leukemia via recognition of genomically imprinted morphologic features. NPJ Precision Oncology, 5(1):38.
Sousa, L. P., Silva, R. R. V., Claro, M. L., Araújo, F. H. D., Borges, R. N., Machado, V. P., and Veras, R. M. S. (2025). Ensemble of cnns for enhanced leukocyte classification in acute myeloid leukemia diagnosis. In Paes, A. and Verri, F. A. N., editors, Intelligent Systems, pages 399–413, Cham. Springer Nature Switzerland.
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE transactions on medical imaging, 35(5):1299–1312.
Thanh, T. T. P., Vununu, C., Atoev, S., Lee, S.-W., and Kwon, K.-R. (2018). Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10:54–58.
Vogado, L. H. S., Veras, R. M. S., and Aires, K. R. T. (2021). Leuknet: Um modelo de rede neural convolucional para o diagnóstico de leucemia. In Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 85–90. Sociedade Brasileira de Computação.
Yamamoto, J. F. and Goodman, M. T. (2008). Patterns of leukemia incidence in the united states by subtype and demographic characteristics, 1997–2002. Cancer Causes & Control, 19(4):379–390.
Publicado
29/09/2025
Como Citar
COELHO, Ana V. S.; SOUSA, Leonardo P.; SILVESTRE, Nicole E. M.; CLARO, Maíla L.; SANTANA, André M.; VERAS, Rodrigo M. S..
Multi-Class Classification of Immature Leukocytes in AML Blood Smears Using an Ensemble of CNNs. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1173-1184.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14441.
