Abstract:
SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has
emerged as a pandemic in recent years. Humans are becoming infected with the virus. In
2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected
people have symptoms that are related to pneumonia, and the virus affects the body’s
respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase
chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits and the
delay caused in obtaining the result suspected patients cannot be treated promptly, resulting
in disease spread. To develop an alternative, radiologists looked at the changes in radiological
imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality.
The suspected patient’s computed tomography (CT) scan is used to distinguish between a
healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep
learning methods have been proposed for COVID-19. The proposed work utilizes CNN
architectures like Xception and InceptionV3. The dataset contains 750 CT scan images
of “COVID” and “Non-COVID” types. The dataset is divided into train and test sets.
Accuracies obtained for Xception is 95%, and InceptionV3 is 93%, respectively. From the
obtained analysis, the results show that the Xception architecture gives better accuracy
compared to InceptionV3 for the quick identification of the COVID-19 patients.
