  Coronavirus Disease 2019 (COVID-19) has spread globally and become a health
crisis faced by humanity since first reported. Radiology imaging technologies
such as computer tomography (CT) and chest X-ray imaging (CXR) are effective
tools for diagnosing COVID-19. However, in CT and CXR images, the infected area
occupies only a small part of the image. Some common deep learning methods that
integrate large-scale receptive fields may cause the loss of image detail,
resulting in the omission of the region of interest (ROI) in COVID-19 images
and are therefore not suitable for further processing. To this end, we propose
a deep spatial pyramid pooling (D-SPP) module to integrate contextual
information over different resolutions, aiming to extract information under
different scales of COVID-19 images effectively. Besides, we propose a COVID-19
infection detection (CID) module to draw attention to the lesion area and
remove interference from irrelevant information. Extensive experiments on four
CT and CXR datasets have shown that our method produces higher accuracy of
detecting COVID-19 lesions in CT and CXR images. It can be used as a
computer-aided diagnosis tool to help doctors effectively diagnose and screen
for COVID-19.
