ArXiv Preprint
Due to the large accumulation of patients requiring hospitalization, the
COVID-19 pandemic disease caused a high overload of health systems, even in
developed countries. Deep learning techniques based on medical imaging data can
help in the faster detection of COVID-19 cases and monitoring of disease
progression. Regardless of the numerous proposed solutions for lung X-rays,
none of them is a product that can be used in the clinic. Five different
datasets (POLCOVID, AIforCOVID, COVIDx, NIH, and artificially generated data)
were used to construct a representative dataset of 23 799 CXRs for model
training; 1 050 images were used as a hold-out test set, and 44 247 as
independent test set (BIMCV database). A U-Net-based model was developed to
identify a clinically relevant region of the CXR. Each image class (normal,
pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian
mixture model. A decision tree was used to aggregate predictions from the
InceptionV3 network based on processed CXRs and a dense neural network on
radiomic features. The lung segmentation model gave the Sorensen-Dice
coefficient of 94.86% in the validation dataset, and 93.36% in the testing
dataset. In 5-fold cross-validation, the accuracy for all classes ranged from
91% to 93%, keeping slightly higher specificity than sensitivity and NPV than
PPV. In the hold-out test set, the balanced accuracy ranged between 68% and
100%. The highest performance was obtained for the subtypes N1, P1, and C1. A
similar performance was obtained on the independent dataset for normal and
COVID-19 class subtypes. Seventy-six percent of COVID-19 patients wrongly
classified as normal cases were annotated by radiologists as with no signs of
disease. Finally, we developed the online service (https://circa.aei.polsl.pl)
to provide access to fast diagnosis support tools.
Wojciech Prazuch, Aleksandra Suwalska, Marek Socha, Joanna Tobiasz, Pawel Foszner, Jerzy Jaroszewicz, Katarzyna Gruszczynska, Magdalena Sliwinska, Jerzy Walecki, Tadeusz Popiela, Grzegorz Przybylski, Andrzej Cieszanowski, Mateusz Nowak, Malgorzata Pawlowska, Robert Flisiak, Krzysztof Simon, Gabriela Zapolska, Barbara Gizycka, Edyta Szurowska, POLCOVID Study Group, Michal Marczyk, Joanna Polanska
2022-10-11
