Detection of COVID-19 lesions based on computed tomography using U-Net 2.5D and GAN

  • José Anatiel Landim UFPI
  • Edson Carvalho UFPI
  • João Otávio Diniz IFPI
  • Alcilene Sousa UFPI
  • Daniel Luz IFPI
  • Antônio Filho UFPI / IFPI


This paper proposes a computational method for automatically detecting suspected regions of COVID-19 from CT scans. COVID-19 has spread rapidly worldwide, infecting over 462 million people and causing over 6 million deaths. There are various methods to diagnose COVID-19, including imaging. The proposed method has five stages, including image acquisition, pre-processing, lung extraction, segmentation of suspected regions using U-Net 2.5D and Pix2Pix architectures, and result validation. The method achieved promising results, with 92% Dice for lung parenchyma segmentation, 82% Dice for suspected region segmentation using U-Net, and 71% Dice using Pix2Pix. It could potentially be integrated into clinical environments as a real aid system.


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LANDIM, José Anatiel; CARVALHO, Edson; DINIZ, João Otávio; SOUSA, Alcilene; LUZ, Daniel; FILHO, Antônio. Detection of COVID-19 lesions based on computed tomography using U-Net 2.5D and GAN. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 83-94. ISSN 2763-8952. DOI: