Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques

• Published in: International journal of biomedical imaging 2022-12, Vol.2022, p.5318447-15
• Main Authors: Islam, Rumana, Tarique, Mohammed
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Alzheimer's disease; Artificial intelligence; Artificial neural networks; Bacterial pneumonia; Chest; Coronaviruses; COVID-19; Data mining; Deep learning; Discriminant analysis; Disease transmission; Feature extraction; Health care facilities; Health facilities; Image classification; Learning algorithms; Lung diseases; Machine learning; Medical imaging; Methods; Neural networks; Pandemics; Patients; Pneumonia; Polymerase chain reaction; Reverse transcription; Severe acute respiratory syndrome coronavirus 2; Tomography; Viruses
• ISSN: 1687-4188; 1687-4196
• DOI: 10.1155/2022/5318447

This paper presents an automated and noninvasive technique to discriminate COVID-19 patients from pneumonia patients using chest X-ray images and artificial intelligence. The reverse transcription-polymerase chain reaction (RT-PCR) test is commonly administered to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer, requires variable time to produce results, and is expensive. Moreover, this test is still unreachable to the significant global population. The chest X-ray images can play an important role here as the X-ray machines are commonly available at any healthcare facility. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis subjectively. This investigation has employed two algorithms to solve this problem objectively. One algorithm uses lower-dimension encoded features extracted from the X-ray images and applies them to the machine learning algorithms for final classification. The other algorithm relies on the inbuilt feature extractor network to extract features from the X-ray images and classifies them with a pretrained deep neural network VGG16. The simulation results show that the proposed two algorithms can extricate COVID-19 patients from pneumonia with the best accuracy of 100% and 98.1%, employing VGG16 and the machine learning algorithm, respectively. The performances of these two algorithms have also been collated with those of other existing state-of-the-art methods.