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.