Avaliação do uso de transfer learning para detecção de tumores cerebrais em imagens médicas

Autores

  • André Gonçalves Jardim UFCSPA
  • Carla Diniz Lopes Becker UFCSPA
  • Thatiane Alves Pianoski UFCSPA
  • Viviane Rodrigues Botelho UFCSPA

DOI:

https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1302

Palavras-chave:

Transfer Learning, Rede Neural Convolucional, Tumor Cerebral

Resumo

Objetivo: Com aumento da viabilidade da aplicação das neurais convolucionais (CNNs) foi objetivado avaliar o uso de desta tecnologia para a detecção de tumores cerebrais em imagens de ressonância magnética computadorizada Método: Foram desenvolvidos dois modelos distintos de CNNs, uma com o uso de Transfer learning e outra sem, para classificar a ocorrência de tumor cerebral. Resultados: foi obtido, com o modelo sem o uso de transfer learning uma acurácia de 99,67%, com sensibilidade de 100% e especificidade de 99,34%; já com o modelo que usou transfer learning, obteve uma acurácia de 98%, com sensibilidade de 98,32% e especificidade de 97,69%. Conclusão: Este estudo destaca a eficácia das CNNs na detecção de tumores cerebrais, sugerindo o uso de sistemas inteligentes como ferramentas de auxílio.

Biografia do Autor

André Gonçalves Jardim, UFCSPA

Master’s Student, Federal University of Health Sciences of Porto Alegre – UFCSPA, Porto Alegre (RS), Brazil.

Carla Diniz Lopes Becker, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

Thatiane Alves Pianoski, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

Viviane Rodrigues Botelho, UFCSPA

Ph.D., Federal University of Health Sciences of Porto Alegre – UFCSPA, DECESA, Porto Alegre (RS), Brazil.

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Publicado

19-11-2024

Como Citar

Jardim, A. G., Becker, C. D. L., Pianoski, T. A., & Botelho, V. R. (2024). Avaliação do uso de transfer learning para detecção de tumores cerebrais em imagens médicas. Journal of Health Informatics, 16(Especial). https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1302

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