CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images

• Published in: Research on biomedical engineering Vol. 39; no. 1; pp. 85 - 98
• Main Authors: Deb, Sagar Deep, Jha, Rajib Kumar, Kumar, Rajnish, Tripathi, Prem S., Talera, Yash, Kumar, Manish
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2023
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Original; Original Article; COVID-19; CoVSeverity-Net; Chest X-Ray; Severity estimation
• ISSN: 2446-4740; 2446-4732; 2446-4740
• DOI: 10.1007/s42600-022-00254-8

Purpose COVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason. Methods We aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe. Results An accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures. Conclusion We strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation.