Abstract
The Coronavirus disease (COVID-19) is an infectious disease that
primarily affects lungs. This virus has spread in almost every
continent. Countries are racing to slow down the spread by testing and
treating patients. To diagnose the infected people, reverse
transcription-polymerase chain reaction (RT-PCR) test is used. Because
of colossal demand; PCR kits are under shortage, and to overcome this;
radiographic techniques such as X-rays and CT-scan can be used for
diagnostic purpose. In this paper, deep learning technology is used to
diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep
learning architecture is used for timely and accurate detection of
coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three
different learning rate strategies are used, such as reducing the
learning rate when model performance stops increasing (reduce on
plateau), cyclic learning rate, and constant learning rate. Reduce on
plateau strategy achieved F1-score of 0.9, cyclic learning rate and
constant learning rate resulted in F1-score of 0.86 and 0.82,
respectively. Implementation is available at
github.com/talhaanwarch/Corona\_Virus/tree/master/CT\_scan