Discriminating COVID-19 from Pneumonia using Machine Learning Algorithms and Chest X-ray Images

Abstract

Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is commonly used to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer and is expensive. Not only that, the diagnostic tests are still unreachable to the majority of the global population. The chest X-ray images are helpful for this purpose as the X-ray machines are available in almost all healthcare facilities. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis. This paper presents automated noninvasive algorithms that can identify the X-ray images of COVID patients from that of pneumonia patients. This investigation has employed two algorithms based on machine learning and deep learning approaches. The lower dimension encoded features are extracted from the X-ray images and machine learning algorithms are applied. On the other hand, the deep learning algorithm relies on the inbuilt feature extractor networks to classify the original X-ray images. The simulation results show that the proposed algorithms can discriminate COVID patients from pneumonia patients with the best accuracies of 100% and 98.1% based on pre-trained deep learning and machine learning algorithms, respectively.