COVID-19 Disease Prediction Utilizing Dilated Convolution Neural Network Based Levy Flight Tunicate Swarm Optimization.

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Abstract

The worldwide pandemic of COVID-19 illness has wreaked havoc on the health and lives of countless individuals in more than 200 countries. More than 44 million individuals have been afflicted by October 2020, with over 1,000,000 fatalities reported. This disease, which is classified as a pandemic, is still being researched for diagnosis and therapy. It is critical to diagnose this condition early in order to save a person’s life. Diagnostic investigations based on deep learning are speeding up this procedure. As a result, in order to contribute to this sector, our research proposes a deep learning-based technique that may be employed for illness early detection. Based on this insight, gaussian filter is applied to the collected CT images and the filtered images are subjected to the proposed tunicate dilated convolutional neural network, whereas covid and non-covid disease are categorized to improve the accuracy requirement. The hyperparameters involved in the proposed deep learning techniques are optimally tuned using the proposed levy flight based tunicate behaviour. To validate the proposed methodology, evaluation metrics are tested and shows superiority of the proposed approach during COVID-19 diagnostic studies.© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.