A novel DeepNet model for the efficient detection of COVID-19 for symptomatic patients

Biomed Signal Process Control. 2021 Jul;68:102812. doi: 10.1016/j.bspc.2021.102812. Epub 2021 May 27.

Abstract

The novel Coronavirus (COVID-19) disease has disrupted human life worldwide and put the entire planet on standby. A resurgence of coronavirus infections has been confirmed in most countries, resulting in a second wave of the deadly virus. The infectious virus has symptoms ranging from an itchy throat to Pneumonia, resulting in the loss of thousands of human lives while globally infecting millions. Detecting the presence of COVID-19 as early as possible is critical, as it helps prevent further spread of disease and helps isolate and provide treatment to the infected patients. Recent radiological imaging findings confirm that lung X-ray and CT scans provide an excellent indication of the progression of COVID-19 infection in acute symptomatic carriers. This investigation aims to rapidly detect COVID-19 progression and non-COVID Pneumonia from lung X-ray images of heavily symptomatic patients. A novel and highly efficient COVID-DeepNet model is presented for the accurate and rapid prediction of COVID-19 infection using state-of-the-art Artificial Intelligence techniques. The proposed model provides a multi-class classification of lung X-ray images into COVID-19, non-COVID Pneumonia, and normal (healthy). The proposed systems' performance is assessed based on the evaluation metrics such as accuracy, sensitivity, precision, and f1 score. The current research employed a dataset size of 7500 X-ray samples. The high recognition accuracy of 99.67% was observed for the proposed COVID-DeepNet model, and it complies with the most recent state-of-the-art. The proposed COVID-DeepNet model is highly efficient and accurate, and it can assist radiologists and doctors in the early clinical diagnosis of COVID-19 infection for symptomatic patients.

Keywords: Artificial intelligence; COVID-19; COVID-DeepNet; Clinical diagnosis; Deep learning; Lung X-rays.