A multi-modal bone suppression, lung segmentation, and classification approach for accurate COVID-19 detection using chest radiographs.

The high transmission rate of COVID-19 and the lack of quick, robust, and intelligent systems for its detection have become a point of concern for the public, Government, and health experts worldwide. The study of radiological images is one of the fastest ways to comprehend the infectious spread and diagnose a patient. However, it is difficult to differentiate COVID-19 from other pneumonic infections. The purpose of this research is to provide an automatic, precise, reliable, robust, and intelligent assisting system 'Covid Scanner' for mass screening of COVID-19, Non-COVID Viral Pneumonia, and Bacterial Pneumonia from healthy chest radiographs. To train the proposed system, the authors of this research prepared novel a dataset called, "COVID-Pneumonia CXR". The system is a coherent integration of bone suppression, lung segmentation, and the proposed classifier, 'EXP-Net'. The system reported an AUC of 96.58% on the validation dataset and 96.48% on the testing dataset comprising chest radiographs. The results from the ablation study prove the efficacy and generalizability of the proposed integrated pipeline of models. To prove the system's reliability, the feature heatmaps visualized in the lung region were validated by radiology experts. Moreover, a comparison with the state-of-the-art models and existing approaches shows that the proposed system finds clearer demarcation between the highly similar chest radiographs of COVID-19 and Non-COVID viral pneumonia. The copyright of "Covid Scanner" is protected with registration number SW-13625/2020. The code for the models used in this research is publicly available at: https://github.com/Ankit-Misra/multi_modal_covid_detection/.