Identification of Severity of Infection for COVID-19 Affected Lungs Images using Elephant Swarm Water Search Algorithm

Mandal, Sudip

Due to the outbreak of the pandemic COVID-19 or ‘novel corona virus disease’, the world is facing a global emergency. In case of severe infection, lungs are affected by COVID-19 significantly, whichmay lead to the death of the patient. In this paper, an automated image-assisted system based on artificial intelligence is proposed to extract infected sections from lung CT scan images that are caused due to COVID-19. Multilevel thresholding is a typical example of maximization problem of optimization to identify the threshold(s) levels for image segmentation. In this paper, Elephant Swarm Water Search Algorithm (ESWSA) has been used for multilevel thresholding based on Otsu’s and Kapur’s method and further analysis of lungs images. It has been observed from the obtained simulated results that ESWSA performs better than other state-of-the-art optimization techniques for multilevel thresholding. Moreover, location of infection and severity of infection has also been extracted from the pixel ratio between the infection and lung sections from the lung CT scan images. It is expected that the proposed methodology will support the doctors as it will reduce diagnostic burden and respective treatment process can be planned faster as the severity of infection by COVID-19 is found by this automated methodology. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Modelling & Simulation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)