Software system to predict the infection in COVID‐19 patients using deep learning and web of things. (24th June 2021)

• Record Type: Journal Article
• Authors: Singh, Ashima; Kaur, Amrita; Dhillon, Arwinder; Ahuja, Sahil; Vohra, Harpreet
• Other Names: Gill Sukhpal Singh guestEditor.; Vinuesa Ricardo guestEditor.; Balasubramanian Venki guestEditor.; Ghosh Soumya K. guestEditor.
• Is Part Of: Software, practice & experience. Volume 52:Number 4(2022)
• Page Start: 868
• Page End: 886
• Publication Date: 2021-06-24
• Subjects: COVID‐19 -- deep learning -- EfficientNet B0 -- SARSCoV‐2 -- segmentation -- U‐net -- WoT; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3011
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F ‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.