Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND: over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of screening tools with high diagnostic accuracy for new illnesses such as COVID-19. In that regard, deep learning approaches have proven as effective solutions for pneumonia classification, especially when considering chest-x-rays images. However, this lung infection can also be caused by other viral, bacterial or fungi pathogens. Consequently, efforts are being poured toward distinguishing the infection source to help clinicians to diagnose the correct disease origin. Following this tendency, this study further explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm.

Authors

  • Danilo Avola
    Department of Computer Science, Sapienza University, Via Salaria 113, 00198 Rome, Italy.
  • Andrea Bacciu
    Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy.
  • Luigi Cinque
    Department of Computer Science, Sapienza University, Via Salaria 113, 00198 Rome, Italy.
  • Alessio Fagioli
    Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy. Electronic address: fagioli@di.uniroma1.it.
  • Marco Raoul Marini
    Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy.
  • Riccardo Taiello
    Department of Computer Science, Sapienza University, Via Salaria 113, Rome 00185, Italy.