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Due to the large accumulation of patients requiring hospitalization, theCOVID-19 pandemic disease caused a high overload of health systems, even indeveloped countries. Deep learning techniques based on medical imaging data canhelp in the faster detection of COVID-19 cases and monitoring of diseaseprogression. Regardless of the numerous proposed solutions for lung X-rays,none of them is a product that can be used in the clinic. Five differentdatasets (POLCOVID, AIforCOVID, COVIDx, NIH, and artificially generated data)were used to construct a representative dataset of 23 799 CXRs for modeltraining; 1 050 images were used as a hold-out test set, and 44 247 asindependent test set (BIMCV database). A U-Net-based model was developed toidentify a clinically relevant region of the CXR. Each image class (normal,pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussianmixture model. A decision tree was used to aggregate predictions from theInceptionV3 network based on processed CXRs and a dense neural network onradiomic features. The lung segmentation model gave the Sorensen-Dicecoefficient of 94.86% in the validation dataset, and 93.36% in the testingdataset. In 5-fold cross-validation, the accuracy for all classes ranged from91% to 93%, keeping slightly higher specificity than sensitivity and NPV thanPPV. In the hold-out test set, the balanced accuracy ranged between 68% and100%. The highest performance was obtained for the subtypes N1, P1, and C1. Asimilar performance was obtained on the independent dataset for normal andCOVID-19 class subtypes. Seventy-six percent of COVID-19 patients wronglyclassified as normal cases were annotated by radiologists as with no signs ofdisease. Finally, we developed the online service (https://circa.aei.polsl.pl)to provide access to fast diagnosis support tools.

CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis