Machine Learning Algorithms for Peripheral Blood Cell Classification - A Hemovision Project Experience


  • Mariana Dourado X. S. Santos Universidade Federal de Goiás
  • William Laus Bertemes Universidade Federal de Goias
  • Iaan Mesquita de Souza Universidade Federal de Goiás
  • Mateus Henrique B. Andrades Universidade Federal de Goiás
  • Vinicius Sebba Patto Universidade Federal de Goiás



Support Vector Machine, Convolutional Neural Networks, White Blood Cells, Machine Learning


This research explores the use of machine learning algorithms to classify nucleated peripheral blood cells. The ResNet18 convolutional neural network was used to pre-process the images and replace the dense layers; and for the output, the Support Vector Machine (SVM) classifier was chosen. Images from different datasets were used for training and testing the model. Thus, the developed model achieved an accuracy and F1-Score of 99.96%. In face of the obtained results, it was found that machine learning algorithms can be satisfactorily integrated into educational and diagnostic support processes.


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How to Cite

Santos, M. D. X. S., Bertemes, W. L., Mesquita de Souza, I., Andrades, M. H. B., & Patto, V. S. (2023). Machine Learning Algorithms for Peripheral Blood Cell Classification - A Hemovision Project Experience. Revista De Informática Teórica E Aplicada, 30(2), 89–100.



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