  The Coronavirus was first emerged in December, in the city of China named
Wuhan in 2019 and spread quickly all over the world. It has very harmful
effects all over the global economy, education, social, daily living and
general health of humans. To restrict the quick expansion of the disease
initially, main difficulty is to explore the positive corona patients as
quickly as possible. As there are no automatic tool kits accessible the
requirement for supplementary diagnostic tools has risen up. Previous studies
have findings acquired from radiological techniques proposed that this kind of
images have important details related to the coronavirus. The usage of modified
Artificial Intelligence (AI) system in combination with radio-graphical images
can be fruitful for the precise and exact solution of this virus and can also
be helpful to conquer the issue of deficiency of professional physicians in
distant villages. In our research, we analyze the different techniques for the
detection of COVID-19 using X-Ray radiographic images of the chest, we examined
the different pre-trained CNN models AlexNet, VGG-16, MobileNet-V2, SqeezeNet,
ResNet-34, ResNet-50 and COVIDX-Net to correct analytics for classification
system of COVID-19. Our study shows that the pre trained CNN Model with
ResNet-34 technique gives the higher accuracy rate of 98.33, 96.77% precision,
and 98.36 F1-score, which is better than other CNN techniques. Our model may be
helpful for the researchers to fine train the CNN model for the the quick
screening of COVID patients.
