Refinamento e Combinação de CNNs para Melhoria na Classificação de Leucócitos Imaturos no Diagnóstico de Leucemia Mieloide Aguda
Resumo
Este artigo aplicou técnicas, como ajuste fino, aumento de dados e comitês em Redes Neurais Convolucionais (Convolutional Neural Networks - CNNs), para a classificação de células leucêmicas em imagens microscópicas de sangue, com foco em leucócitos imaturos. O principal objetivo é classificar essas células em quatro subtipos distintos presentes na base de dados, que contém um total de 5.738 imagens de esfregaços de sangue. Utilizando a técnica de comitês com voto majoritário em três combinações diferentes de bases de dados, o estudo alcançou uma acurácia de 84,37% com o comitê formado pelas CNNs MobileNetV2, EfficientNetB3 e DenseNet201.
Referências
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Bain, B. J. (2010). Myeloid and lymphoid neoplasms with eosinophilia and abnormalities of pdgfra, pdgfrb or fgfr1. Haematologica.
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.
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., and Prior, F. (2013). The cancer imaging archive (tcia): Maintaining and operating a public information repository. Journal of Digital Imaging.
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.
Labati, R. D., Piuri, V., and Scotti, F. (2011). All-idb: the acute lymphoblastic leukemia image database for image processing. Proc. of the 2011 IEEE Int. Conf. on Image Processing (ICIP 2011).
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. 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.
Prinyakupt, J. and Pluempitiwiriyawej, C. (2015). Segmentation of white blood cells and comparison of cell morphology by linear and naïve bayes classifiers. BioMed Eng OnLine.
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, I., 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, Porto Alegre, RS, Brasil. SBC.
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.-H., and Kwon, K.-R. (2018). Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10:54–58.
Travlos, G. S. (2006). Normal structure, function, and histology of the bone marrow., volume 34.
Vogado, L., Veras, R., Andrade, A., Santos, L., Aires, K., and Machado, V. (2017). Um sistema de diagnóstico de leucemia utilizando cnn’s pré-treinadas e um comitê de classificadores. In Anais do XVII Workshop de Informática Médica.
Bain, B. J. (2010). Myeloid and lymphoid neoplasms with eosinophilia and abnormalities of pdgfra, pdgfrb or fgfr1. Haematologica.
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.
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., and Prior, F. (2013). The cancer imaging archive (tcia): Maintaining and operating a public information repository. Journal of Digital Imaging.
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.
Labati, R. D., Piuri, V., and Scotti, F. (2011). All-idb: the acute lymphoblastic leukemia image database for image processing. Proc. of the 2011 IEEE Int. Conf. on Image Processing (ICIP 2011).
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. 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.
Prinyakupt, J. and Pluempitiwiriyawej, C. (2015). Segmentation of white blood cells and comparison of cell morphology by linear and naïve bayes classifiers. BioMed Eng OnLine.
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, I., 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, Porto Alegre, RS, Brasil. SBC.
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.-H., and Kwon, K.-R. (2018). Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10:54–58.
Travlos, G. S. (2006). Normal structure, function, and histology of the bone marrow., volume 34.
Vogado, L., Veras, R., Andrade, A., Santos, L., Aires, K., and Machado, V. (2017). Um sistema de diagnóstico de leucemia utilizando cnn’s pré-treinadas e um comitê de classificadores. In Anais do XVII Workshop de Informática Médica.
Publicado
11/09/2024
Como Citar
MORICONI, Émery F.; SOUSA, Leonardo P.; CLARO, Maila; ROCHA, Ana A. F.; VERAS, Rodrigo M. S..
Refinamento e Combinação de CNNs para Melhoria na Classificação de Leucócitos Imaturos no Diagnóstico de Leucemia Mieloide Aguda. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 12. , 2024, Parnaíba/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 129-138.
DOI: https://doi.org/10.5753/ercemapi.2024.243682.
