Empirical Analysis of Machine Learning and Deep Learning Techniques for COVID-19 Detection Using Chest X-rays

Abstract

Due to the Coronavirus (COVID-19) cases growing rapidly, the effective screening of infected patients is becoming a necessity. One such way is through chest radiography. With the high stakes of false negatives being potential cause of innumerable more cases, expert opinions on x-rays are high in demand. In this scenario, Deep Learning and Machine Learning techniques offer fast and effective ways of detecting abnormalities in chest x-rays and can help in identifying patients affected by COVID-19. In this paper, we did comparative analysis of various Machine Learning and Deep Learning techniques on chest x-rays based on accuracy, precision, recall, f1 score, and Matthews correlation coefficient. It was observed that improved results were obtained using Deep Learning.