Diagnosis of COVID-19 Infected Lungs from Chest X-ray Images using Deep Learning Algorithms

Abstract

Severe Acute Respiratory Symptom Coronavirus 2 (SARS-CoV-2) is newly discovered as a beta coronavirus. The
virus-induced unexplained etiological pneumonia and is referred to as the 2019 Coronavirus Disease (COVID-19). Though the disease has appeared in a new way, there is no medication for transited patients. So, for diagnosing the COVID-19 infected lungs from X-Ray images, an automated technique has been suggested in this manuscript. The proposed system is divided into two stages: Image Acquisition and Selection of Algorithms. In the IAA, the training data's size has been increased by augmenting the image in different ways. The Algorithm Selection portion explained the Convolutional Neural Network (CNN) and VGG19.
The Tuning of hyperparameters section determines the precise hyperparameter combination in order to maximize the model's performance. In this study, CNN and VGG19 are used and found accuracy scores of 97% and 67%, respectively. The comparative analysis shows that the propound method acts better than the solution that exists. Eventually, Precision, Recall, and F1 score have been extracted and interpreted the model's loss functions in the research. This research has carried out by focusing on essential aspects in terms of COVID-19. Therefore, for the diagnosis of coronavirus infection, the technique can be used effectively.