Aprendizado Profundo para Detecção de Cálculos Renais em Imagens de Tomografia Computadorizada

  • Thiago Lima UFPI
  • Camila Catiely UFPI
  • Vitoria Sousa UFPI
  • Rodrigo Veras UFPI
  • Flávio Araújo UFPI

Resumo


A calcificação renal é uma doença comum, geralmente detectada pelos médicos especialistas em exames de tomografia computadorizada (TC). Contudo, a análise desse tipo de exames ainda é uma tarefa desafiadora, o que torna um trabalho repetitivo, fadigante e sujeito a erros. Neste contexto propomos um método para detecção de calcificações, na região renal, em imagens de TC, que pode facilitar a tomada de decisão do especialista. Para o desenvolvimento desse método foi utilizada uma base de imagens privada com 887 amostras provenientes de 20 exames de diferentes pacientes. As imagens foram segmentadas utilizando a Mask-RCNN e para etapa de detecção das calcificação, avaliamos quatro modelos YOLOv5 com diferentes tamanhos da imagem de entrada. O modelo que se destacou foi a YOLOv5n com tamanho de entrada 640×640, obtendo um mAP_50 de 0,89, precisão de 0,87 e recall de 0,85.

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Publicado
27/06/2023
LIMA, Thiago; CATIELY, Camila; SOUSA, Vitoria; VERAS, Rodrigo; ARAÚJO, Flávio. Aprendizado Profundo para Detecção de Cálculos Renais em Imagens de Tomografia Computadorizada. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 47-58. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2023.229430.

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