Artificial intelligence-based solutions for early identification and classification of COVID-19 and acute respiratory distress syndrome
COVID-19 has spread all over the globe; the initial case was detected at the end of 2019. The identification of disease at an early stage is needed to provide proper medication and isolate patients to preventing the spread of virus. This chapter focuses on the application of an artificial intelligence-based enhanced kernel support vector machine (E-KSVM) approach to detect COVID-19 and acute respiratory distress syndrome (ARDS). KSVM is enhanced by the use of the particle swarm optimization algorithm to tuning the parameters of KSVM. First, preprocessing takes place to remove unwanted details and noise. This is followed by the Hough transform to extract useful features from the image. Finally, the E-KSVM model is applied to classify images into normal, COVID-19, and ARDS. An extensive set of experimentations takes place on a chest X-ray dataset and ensures that the E-KSVM model has the ability to detect the disease effectively. The simulation outcome indicates that the E-KSVM model attains a maximum sensitivity of 72.34%, specificity of 75.20%, accuracy of 74.01%, and F score of 73.94% with a minimum computation time of 8.039s.
Data Science for COVID-19 Volume 1: Computational Perspectives
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Sujathakrishamoorthy; Mohan, Surapaneni Krishna; Priya, Veeraraghavan Vishnu; Gayathri, R.; and Shiny, M. Lorate, "Artificial intelligence-based solutions for early identification and classification of COVID-19 and acute respiratory distress syndrome" (2021). Kean Publications. 1031.