Transfer learning approch for detecting covid-19 using chest X- Ray images

Abstract

Due to the (COVOD-19 coronavirus, the entire world is undergoing a pandemic. Coronavirus 2 produces severe acute respiratory illness. This virus is discovered in December 2019 in China, Wuhan. As we are experiencing, the affected patients are expanding at a rapid rate. The World Health Organization (WHO) has recommended that testing be done as much as possible to recognize those who are affected and those who are carriers of this disease. However, the main issue here is the scarcity of COVID-19 testing kits and trained people to perform the testing in a pandemic situation. However, a lot of research was seeking workaround solutions for detecting the COVID-19. As a result of these projects, a few papers were polished for detecting the COVID-19 based on chest Xray scan images. However, most of the research has used vanilla CNN, which makes the

test more reliable and convenient. But we have some practical issues in the application of

traditional CNN. Basically, CNN is a supervised learning method, and it takes more time

for the learning process. And in general, CNN works well for larger datasets. However,

the chest

X-ray images are limited in practice, we propose combining transfer learning and ensemble learning techniques to achieve excellent accuracy while spending the least amount of time possible on the entire learning process. This study mainly focuses on the CNN based pre-trained models such as DenseNet201, EfficientNetB7 and VGG16 for increasing the accuracy level of the model, which makes the test reliable and more trustworthy.