  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.
