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After more than two years since the beginning of the COVID-19 pandemic, thepressure of this crisis continues to devastate globally. The use of chest X-ray(CXR) imaging as a complementary screening strategy to RT-PCR testing is notonly prevailing but has greatly increased due to its routine clinical use forrespiratory complaints. Thus far, many visual perception models have beenproposed for COVID-19 screening based on CXR imaging. Nevertheless, theaccuracy and the generalization capacity of these models are very muchdependent on the diversity and the size of the dataset they were trained on.Motivated by this, we introduce COVIDx CXR-3, a large-scale benchmark datasetof CXR images for supporting COVID-19 computer vision research. COVIDx CXR-3 iscomposed of 30,386 CXR images from a multinational cohort of 17,026 patientsfrom at least 51 countries, making it, to the best of our knowledge, the mostextensive, most diverse COVID-19 CXR dataset in open access form. Here, weprovide comprehensive details on the various aspects of the proposed datasetincluding patient demographics, imaging views, and infection types. The hope isthat COVIDx CXR-3 can assist scientists in advancing machine learning researchagainst both the COVID-19 pandemic and related diseases.

COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics