Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images

Mughees Ahmad; Usama Ijaz Bajwa; Yasar Mehmood; Muhammad Waqas Anwar

doi:10.1007/s00521-023-08200-0

Lightweight ResGRU: a deep learning-based prediction of SARS-CoV-2 (COVID-19) and its severity classification using multimodal chest radiography images

Neural Comput Appl. 2023;35(13):9637-9655. doi: 10.1007/s00521-023-08200-0. Epub 2023 Jan 24.

Authors

Mughees Ahmad¹, Usama Ijaz Bajwa¹, Yasar Mehmood¹, Muhammad Waqas Anwar¹

Affiliation

¹ Machine Perception and Visual Intelligence Research Group, Department of Computer Science, COMSATS University Islamabad, Lahore Campus. 1.5 KM Defence Road off Raiwind Road, Lahore, Pakistan.

Abstract

The new COVID-19 emerged in a town in China named Wuhan in December 2019, and since then, this deadly virus has infected 324 million people worldwide and caused 5.53 million deaths by January 2022. Because of the rapid spread of this pandemic, different countries are facing the problem of a shortage of resources, such as medical test kits and ventilators, as the number of cases increased uncontrollably. Therefore, developing a readily available, low-priced, and automated approach for COVID-19 identification is the need of the hour. The proposed study uses chest radiography images (CRIs) such as X-rays and computed tomography (CTs) to detect chest infections, as these modalities contain important information about chest infections. This research introduces a novel hybrid deep learning model named Lightweight ResGRU that uses residual blocks and a bidirectional gated recurrent unit to diagnose non-COVID and COVID-19 infections using pre-processed CRIs. Lightweight ResGRU is used for multi-modal two-class classification (normal and COVID-19), three-class classification (normal, COVID-19, and viral pneumonia), four-class classification (normal, COVID-19, viral pneumonia, and bacterial pneumonia), and COVID-19 severity types' classification (i.e., atypical appearance, indeterminate appearance, typical appearance, and negative for pneumonia). The proposed architecture achieved f-measure of 99.0%, 98.4%, 91.0%, and 80.5% for two-class, three-class, four-class, and COVID-19 severity level classifications, respectively, on unseen data. A large dataset is created by combining and changing different publicly available datasets. The results prove that radiologists can adopt this method to screen chest infections where test kits are limited.

Keywords: COVID-19; COVID-19 severity levels; Chest radiography images; Deep learning; Residual blocks; SARS-CoV-2.

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