Predictive Analysis of Covid-19 Disease Severity in X-ray images: using Deep Learning Techniques

Abstract

Healthcare systems are evolving in order to deal with the issues of death in human. The most current worldwide pandemic, COVID-19, which first appeared in 2019, has spread throughout the world. Covid sickness is currently one of the leading causes of death in humans. The signs of COVID-19 include fever, coughing, exhaustion, body pains, and shortness of breath. These symptoms can range in severity from moderate to severe. Also possible for some people are sore throats, congestion, runny noses, and loss of taste or smell. The COVID-19 pandemic has prompted researchers to create imaging-based medical treatments, allowing medical staff to detect COVID-19-infected patients more quickly and begin necessary treatments on schedule. The new coronavirus (COVID-19) illness is extremely contagious, thus there are often too many patients waiting in line for chest X-rays. This burdens the radiologists and physicians and has a detrimental impact on the patient's treatment and pandemic management. Due to this highly contagious condition, there aren't as many clinical amenities available, such as hospitals with critical care units and ventilatory machines, it is now crucial to categorise the patients according to their severity levels. Using deep learning techniques, we categorized the individual based on the severity levels of moderate, severe, and extreme if they tested positive for COVID-19. The COVID-19 patient severity divided into three groups: moderate, serious, and extreme, using Convolution Neural Network (CNN) three architecture: VGG19, ResNet-50 and DenseNet201 model that was constructed with an average accuracy of VGG19-89.63%, ResNet-50 with 92.62% and DenseNet201 with 96.4% with the input of chest X-ray pictures.