Comparative Analysis of Deep Learning Algorithms for Classification of COVID-19 X-Ray Images (2110.09294v1)

Abstract: 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 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.