A Deep Ensemble Model for Differential Diagnosis of Covid-19 Pneumonia using Thoracic Radiography

ABSTRACT

The successful elimination of the SARS-Cov2 virus has evaded the society and medical fraternity to date. Months have passed but the virus is still very much present amongst us though its severity and contagiousness have decreased. The pandemic which was first detected in Wuhan, China in late 2019 has had colossal ramifications for the societal, financial and physical well-being of humankind. Timely detection and isolation of infected persons is the only way to contain this contagion. One of the biggest hurdles in accurately detecting Covid-19 is its similarities to other thoracic ailments such as Lung cancer, bacterial and viral Pneumonia, tuberculosis and others. Differential observation is challenging due to identical radioscopic discoveries such as GGOs, crazy paving structures and their combinations. Thorax imaging such as X-rays(CXR) have proven to be an efficient and economical diagnostics for detecting Covid-19 Pneumonia. The proposed work aims at utilising three CNN models namely Inception-V3, DenseNet169 and VGG16 along with feature concatenation and Ensemble technique to correctly predict Covid-19 Pneumonia from Chest X-rays of patients. The Covid-19 Radiography dataset, having a total of 4839 CXR images, has been employed to evaluate the proposed model and accuracy, precision, recall and F1-Score of 97.74%, 97.78%, 97.73% and 97.75% has been obtained. The proposed system can assist medical professionals in detecting Covid-19 from a host of other pulmonary diseases with a high probability. © 2023 IEEE.