Open AccessCOVID-19 automatic diagnosis with CT images using the novel Transformer architecture
Author(s) -
Gabriel B. Costa,
Anselmo Cardoso de Paiva,
Geraldo Bráz,
Marco Melo Ferreira
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbcas.2021.16073
Subject(s) - transformer , covid-19 , artificial intelligence , computer science , pandemic , quarantine , computer vision , medicine , engineering , pathology , infectious disease (medical specialty) , electrical engineering , disease , voltage
Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the rst tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modied vision transformer, reaching 96.00% accuracy.