Swin Transformer for COVID-19 Infection Percentage Estimation from CT-Scans

Coronavirus disease 2019 (COVID-19) is an infectious disease that has spread globally, disrupting the health care system and claiming millions of lives worldwide. Because of the high number of Covid-19 infections, it has been challenging for medical professionals to manage this crisis. Estimating the Covid-19 percentage can help medical staff categorize patients by severity and prioritize accordingly. With this approach, the intensive care unit (ICU) can free up resuscitation beds for the critical cases and provide other treatments for less severe cases to efficiently manage the healthcare system during a crisis. In this paper, we present a transformer-based method to estimate covid-19 infection percentage for monitoring the evolution of the patient state from computed tomography scans (CT-scans). We used a particular Transformer architecture called Swin Transformer as a backbone network to extract the feature from the CT slice and pass it through multi-layer perceptron (MLP) to obtain covid-19 infection percentage. We evaluated our approach on the covid-19 infection percentage estimation challenge dataset, annotated by two expert radiologists. The experimental results show that the proposed method achieves promising performance with a mean absolute error (MAE) of 4.5042, Pearson correlation coefficient (PC) of 0.9490, root mean square error (RMSE) of 8.0964 on the given Val set leaderboard and a MAE of 3.5569, PC of 0.8547 and RMSE of 7.5102 on the given Test set Leaderboard. These promising results demonstrate the high potential of Swin Transformer architecture for this image regression task of covid-19 infection percentage estimation from CT-scans. The source code of this project can be found at: https://github.com/suman560/Covid-19-infection-percentage-estimation .