Fish Erythrocytes Nuclear Abnormalities Classification using Machine Learning

  • Newton Loebens UCDB
  • Bruno do Amaral Crispim UFGD
  • Nathalya Alice de Lima UFGD
  • Everton Tetila UFGD
  • Celso Costa IFMS
  • Willian Paraguassu Amorim UFGD
  • Alexeia Barufatti UFGD
  • Pedro Henrique Neves da Silva UFMS
  • Gabriel Toshio Hirokawa Higa UCDB
  • Hemerson Pistori UFMS


The creation of automated systems capable of detecting anomalies in fish erythrocytes is an important concern in the area of marine biology. We investigate the possibility of using machine learning to classify images of abnormal and normal nuclei of fish erythrocytes, considering three abnormalities: nuclear bud, notched nuclei, and vacuole nuclei, among others. Random Forests were shown to have the highest AUC median in both sets, reaching AUC values of 0.896 and 0.959 for all sets of classes and the vacuole set, respectively, being able to correctly classify a high percentage of the bud and notched cells. However, when all classes are considered, the outcome is impressively better.


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LOEBENS, Newton et al. Fish Erythrocytes Nuclear Abnormalities Classification using Machine Learning. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 96-101. DOI: