COVIDPRO-NET: a prognostic tool to detect COVID 19 patients from lung X-ray and CT images using transfer learning and Q-deformed entropy

Abstract

ABSTRACT The humankind had faced several pandemic outbreaks, and coronavirus illness (COVID-19) caused by severe, acute respiratory syndrome coronavirus 2, is designated an emergency by the World Health Organization (WHO). Recognition of COVID-19 is a challenging task. The most commonly used methods are X-ray and CT scans images to inspect COVID-19 patients. It requires specialised medical professionals to report each patient’s health manually. It is found that COVID-19 shows considerable similarity to pneumonia lung disease. Thus, knowledge learned from a model to diagnose pneumonia can be translated to identify COVID-19. Transfer learning method offers a drastic performance when compared with results from conventional classification. In this study, Image pre-processing is done to alleviate intensity variations between medical images. These processed images undergo a feature extraction which is accomplished using Q-deformed entropy and deep learning extraction. The feature extraction techniques are employed to remove abnormal markers from images, noise impedance from tissues and lesions. The traits acquired are integrated to differentiate between COVID-19, pneumonia and healthy cases. The primary aim of this model is to produce an image processing tool for medical professionals. The model results to inspect how a healthy or COVID-19 individual outperforms conventional models. The maximum accuracy of the collected data set is 99.68%.