Classification of COVID-19 InfectedX-Ray Image using deep Learning Techniques

Abstract

In 2019,Coronavirus disease emerged as a pandemic all over world thus named as COVID-19. It has a greater impact on the global scenario in recent times. Its effect started in the year of 2019 from Wuhan,China. This ongoing pandemic situation has attracted the attention of medical professionals, technocrats, and researchers to work together aiming towards the reduction of spreading and death. Though enough evidence is not found, still the imaging technique has occupied an important role to diagnose the disease. Due to the limited availability of images, it becomes a challenge for researchers. In this piece of work, authors have tried to detect infected personnel using theconvolutional neural network (CNN) based classification model. Out of many variants of CNN classifiers, SqueezeNet and GoogleNet have been utilized for the classification of human chest X-ray images. A total of 1811 X-ray samples are used as training  and testing input to the model. It is found that GoogleNet results better and tuned a well classifier in comparison to that of the SqueezeNet model. The performancemeasure like accuracy, sensitivity, specificity, and jaccard are evaluated and are shown in the result section.