XCovNet: A wide deep learning model for CXR-based COVID-19 detection

Abstract

The high death toll and economic impact of the COVID-19 pandemic emphasize the need for effective population screening technologies. The high cost, limited availability, and slow nature of CT scans and PCR-based tests renders them impractical for frequent use among the general public. As chest X-ray (CXR) imaging is fast and economical, a high accuracy CXR-based test would be well-suited for such screenings. Deep learning algorithms are widely used to aid medical image diagnoses. We use a collection of state-of-the-art pre-trained deep neural network models with additional layers to detect the COVID-19 cases from a sample of healthy, COVID-19, and pneumonia patients. We observed models trained with concatenated features of multiple pre-trained deep learning architectures outperform the individual and ensemble models. Our final model obtained a recall of greater than 98% and a precision of greater than 95% on two separate datasets. The wide architecture of xCovNet may contribute to its robust behavior, providing a systematic approach to construct a reliable deep learning model for emerging datasets.