Detection of COVID-19 based on Deep Learning

COVID-19, often known as the coronavirus sickness of 2019, is a fast-spreading virus-related disease that has affected masses of people worldwide. Given its rapid spread and growing number, the healthcare personnel are finding it impossible to manage. Determine the disease and stop it from spreading. Consequently, it changed into the diagnostic procedure must be automated. This effectiveness will help to prevent the spread of the infection among healthcare personnel. One of the more recent academic fields that can address these concerns is medical image analysis. This essay contrasts and compares the utilization of Current deep learning models include VGG16, VGG19, DenseNet121, DenseNet169 and DenseNet201 to cope with the identification and classification of from cases of non-covid and covid-19. This study uses 7200 chest X-ray images from individuals with non-covid (3600) and COVID-19 (3600). Each technique was examined by means of performance and confusion metrics. Results indicate VGG series when compared to the other pretrained model performance was better.