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A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

Joshi Rakesh Chandra, Yadav Saumya, Pathak Vinay Kumar, Malhotra Hardeep Singh, Khokhar Harsh Vardhan Singh, Parihar Anit, Kohli Neera, Himanshu D., Garg Ravindra K., Bhatt Madan Lal Brahma, Kumar Raj, Singh Naresh Pal, Sardana Vijay, Burget Radim, Alippi Cesare, Travieso-Gonzalez Carlos M., Dutta Malay Kishore

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 1

239--254

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.