| dc.description.abstract | With the rapid growth of Severe Acute
Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or
Covid-19 into a pandemic, quick and efficient
alternative testing methods were needed. Although
Viral Nucleic Acid tests are the primary and standard
method of testing, the time-consuming process, and
the lack of availability of test kits in certain areas
have been problematic for the quick diagnosis of the
disease. Therefore, using radiologic modalities such as
chest X- rays and Computerized Tomography (CT)
were studied due to their wider availability because of
their usage in the diagnosis of other diseases. This
research is based on chest X-rays and tests the usage of
deep multitask convolutional neural networks (CNN)
to detect both Covid-19 and Covid-19 related
pneumonia conditions in a patient simultaneously.
Usage of chest X-rays allows for wider availability for
usage in rural areas, where computerized
tomography facilities are rare. Current results from
separate custom CNN models with same layer
structure but different task specific features, give an
accuracy of 94% on Covid-19 detection and 90%
accuracy on Covid-19 pneumonia detection. As a
novelty, this paper suggests that a multitask learning
based CNN model in the same architecture would be
viable to detect both conditions from a single neural
network, simultaneously. The simultaneous detection
of Covid-19 and Covid-19 pneumonia in a patient is
a further extension to traditional testing methods and
allows for more effective treatments from the
beginning. | en_US |
