Covid19 and Pneumonia Detection Using Deep-Leaning

Abstract

Abstract: It became clear that humanity must learn to live with and adapt to any pandemic such has Covid - 19 , especially in light of the fact that the vaccines currently in development do not prevent the infection but only lessen the intensity of the symptoms. It is crucial to diagnose both pneumonia and COVID-19 since they both have an impact on the lungs. In this study, the automatic detection of the Coronavirus disease was carried out using a data-set of X-ray pictures from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal occurrences.The current approach for detection and diagnosis of COVID-19 is the RT-PCR and rapid test as known rapidtest is not so effective and RT-PCR is time-consuming and, in lots of instances, no longer less expensive as a consequence the development of new low-price rapid tests of diagnostic gear to aid medical evaluation is needed. The study’s objective is to assess the effectiveness of cutting- edge convolutional neural network designs for medical picture categorization that have been recently suggested. In particular, the Transfer Learning method was utilised. Transfer learning makes it possible to detect many problems in small collectionsof medical image data, frequently with outstanding outcomes. The data sets used in this investigation are a collection of 5144 X-ray images, including 460 images with verified Covid-19 illness,3418 photos with confirmed common bacterial pneumonia, and 1266 images of healthy conditions. The information was gatheredfrom X-ray pictures that were accessible in public medical repositories. According to the data, Deep Learning combined with X-ray imaging may be able to identify important biomarkers for the Covid-19 disease. CNN achieved the highest accuracy99.49% and specificity, followed by VGG-16 at 67.19% and densenet at 91.94