Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVES: Pneumonia is a disease that affects the lungs, making breathing difficult. Nowadays, pneumonia is the disease that kills the most children under the age of five in the world, and if no action is taken, pneumonia is estimated to kill 11 million children by the year 2030. Knowing that rapid and accurate diagnosis of pneumonia is a significant factor in reducing mortality, acceleration, or automation of the diagnostic process is highly desirable. The use of computational methods can decrease specialists' workload and even offer a second opinion, increasing the number of accurate diagnostics.

Authors

  • Vandecia Fernandes
    Federal University of Maranhão, Applied Computing Group - NCA, Av. dos Portugueses, 1996, Campus do Bacanga, São Luís, Maranhão 65080-805, Brazil. Electronic address: vandecia@nca.ufma.br.
  • Geraldo Braz Junior
    Federal University of Maranhão, Applied Computing Group - NCA, Av. dos Portugueses, 1996, Campus do Bacanga, São Luís, Maranhão 65080-805, Brazil.
  • Anselmo Cardoso de Paiva
    Applied Computing Group - NCA, Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil.
  • Aristófanes Corrêa Silva
    Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil. Electronic address: ari@dee.ufma.br.
  • Marcelo Gattass
    Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. São Vicente, 225, Gávea 22453-900, Rio de Janeiro, RJ, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br.