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

Abstract

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 are publicly available at: https://github.com/Ankit-Misra/multi_modal_covid_detection/ . • Preparing a benchmarking dataset, “COVID Pneumonia CXR”, comprising of bone suppressed and lung segmented CXRs. The dataset is validated by radiology experts. • Utilizing the potential of the Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) for developing an automatic system for COVID-19 screening from non-COVID viral pneumonia, bacterial pneumonia, and healthy using chest radiographs. • Coherent integration of bone suppression, lung segmentation, and cGAN based classifier with a user friendly web application to develop the precise, low-cost, reliable, convenient, robust, and intelligent assisting system ‘COVID Scanner’ for mass screening of COVID-19. • Analyzing the impact of augmentation, bone suppression and lung segmentation on the performance of classifier. • Identifying the loss function useful for resolving the problem of class imbalance. • Validating the reliability of the model by generating feature heatmaps using GradCam++. • Validation of the screening system by the Radiology Experts involved in this research.