Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples Moreover, these methods can neither localize nor grade the severity of COVID-19 infection For this purpose, recent studies proposed to explore the activation maps of deep networks However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83 20%, which is significantly superior to the activation maps created by the previous methods Finally, the proposed approach achieved a COVID-19 detection performance with 94 96% sensitivity and 99 88% specificity
COVID-19 infection map generation and detection from chest X-ray images