Classificação automática de glóbulos brancos usando descritores de forma e textura e eXtreme Gradient Boosting

  • Domingos A. Dias Júnior UFMA
  • Luana B. da Cruz UFMA
  • João O. B. Diniz UFMA / IFMA
  • Geraldo Braz Júnior UFMA
  • Aristófanes C. Silva UFMA

Resumo


O diagnóstico de doenças sanguíneas envolve a identificação e caracterização de amostras de sangue de pacientes pela contagem e classificação de glóbulos brancos. Métodos automatizados têm importantes aplicações para auxiliar médicos. O objetivo deste trabalho é desenvolver um método para classificação automática de glóbulos brancos utilizando técnicas de realce, Threshold Adjacency Statistics (TAS) para extração de características e eXtreme Gradient Boosting (XGBoost) para classificação. Os resultados são promissores comparados a outras técnicas e trabalhos da literatura, alcançado 93,27% de acurácia e 90% de F-Measure. Com isto, acredita-se que o método possa auxiliar especialista nesta tarefa importante.

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Publicado
15/06/2021
DIAS JÚNIOR, Domingos A.; CRUZ, Luana B. da; DINIZ, João O. B.; BRAZ JÚNIOR, Geraldo; SILVA, Aristófanes C.. Classificação automática de glóbulos brancos usando descritores de forma e textura e eXtreme Gradient Boosting. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 95-106. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2021.16056.

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